Trustmodel MCP

TrustModel MCP Server

A Model Context Protocol (MCP) server that lets any AI agent call TrustModel for trust evaluation, safety/bias analysis, and end-to-end agentic trace evaluation.

Works with Claude Code, Cursor, Windsurf, Claude Desktop, and any other MCP-compatible client.

Quick Start

1. Get an API key

Sign up at app.trustmodel.ai and create an API key under Settings → API Keys. Keys have the format tm-{env}-{keyid}_{secret} (e.g. tm-prod-abc12345_0123456789abcdef…).

2. Configure your MCP client

Claude Code

claude mcp add trustmodel \

--env TRUSTMODEL_API_KEY=tm-prod-xxxx_yyyy \ -- npx -y @trustmodel/mcp-server

Cursor / Windsurf

Add to your MCP configuration file (.cursor/mcp.json or equivalent):

{

"mcpServers": { "trustmodel": { "command": "npx", "args": ["-y", "@trustmodel/mcp-server"], "env": { "TRUSTMODEL_API_KEY": "tm-prod-xxxx_yyyy" } } } }

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{

"mcpServers": { "trustmodel": { "command": "npx", "args": ["-y", "@trustmodel/mcp-server"], "env": { "TRUSTMODEL_API_KEY": "tm-prod-xxxx_yyyy" } } } }

Environment Variables

Tool profiles

To stay within the 5–8 tool best-practice budget (more tools degrade an agent's tool selection), the server exposes a small default set and keeps advanced tools opt-in.

Default profile (6 tools) — the daily drivers: trustmodel_evaluate_local · trustmodel_score · trustmodel_trace_start · trustmodel_trace_step · trustmodel_trace_finalize · trustmodel_govern

Advanced — set TRUSTMODEL_PROFILE=security (or advanced / all, or TRUSTMODEL_ADVANCED_TOOLS=true) to additionally expose: trustmodel_evaluate (cloud batch), trustmodel_credits, trustmodel_upload_trace, trustmodel_evaluate_agent, trustmodel_score_agent, trustmodel_mcp_scan_server, trustmodel_shadow_discovery_*, trustmodel_redteam_*, and trustmodel_shadowai_* — 20 tools total.

claude mcp add trustmodel --env TRUSTMODEL_PROFILE=security -- npx -y @trustmodel/mcp-server

Tools

The server exposes 18 tools across six areas. Use this table to pick the right one; full input/output docs follow below.

Shadow Discovery tools (trustmodel_shadow_discovery_*) touch the local filesystem. They are always listed, but return a skip report unless TRUSTMODEL_AGT_DISCOVERY_ENABLED=true is set on the server.

Classic evaluation

trustmodel_evaluate

Create a batch evaluation run against a specified AI model. The backend runs a comprehensive suite (safety, bias, accuracy, hallucination, reasoning, etc.) and returns an id you can poll with trustmodel_score.

Inputs:

• model_identifier (string, required) — e.g. "gpt-4o", "claude-sonnet-4-5". Discover via GET /sdk/v1/models/.
• vendor_identifier (string, required) — e.g. "openai", "anthropic", "google".
• api_key (string, optional) — Vendor API key for BYOK. Omit to use TrustModel's platform key. Do not pass a TrustModel API key here — that goes in the TRUSTMODEL_API_KEY env var.
• categories (string[], optional) — Category names to evaluate. Only honored when evaluation_type is "Custom" or "Score Only".
• evaluation_type (string, optional, default "Custom") — One of "Custom", "Score Only", "Comprehensive", "Limited", "Quick Scan".
• application_type (string, optional, default "generic") — chatbot, knowledge-agent, creation-tool, document-repository, analysis-tool, automation-agent, generic.
• user_personas (string[], optional, default ["external-customer"]) — Any of external-customer, internal-employee, technical-user, domain-expert, vulnerable-groups, generic.
• application_description (string, optional).
• domain_expert_description (string, optional) — When user_personas includes "domain-expert". One of "cross-domain" (default), "medical", "commercial_banking".
• model_config_name (string, optional) — Display name for this run.
• template_id (UUID, optional), template_name (string, optional) — Reuse or rename an existing evaluation template.

trustmodel_score

Fetch the detail (status, completion %, scores) for an evaluation created via trustmodel_evaluate.

Inputs:

• evaluation_id (integer or numeric string, required) — The id returned by trustmodel_evaluate.

trustmodel_credits

Check remaining API credit balance. No inputs.

Agentic trace evaluation

TrustModel evaluates AI agents by consuming their execution trace (thoughts, tool calls, tool results, responses) and scoring them across 4 categories: tool_use_accuracy, reasoning_quality, goal_completion, safety_compliance.

There are two ways to submit a trace — streaming (preferred for live agents) and one-shot (when you have a pre-assembled trace).

Streaming capture (preferred)

Open a session, record steps as the agent works, finalize at the end. Finalize uploads to cloud storage and auto-creates the evaluation run.

trustmodel_trace_start

Open a new trace session.

Inputs:

• goal (string, required) — What the agent is trying to achieve.
• name (string, required) — Display name for the evaluation run.
• agent_framework (string, required) — e.g. "langchain", "crewai", "claude-code", "custom".
• agent_model (string, optional) — e.g. "gpt-4o", "claude-sonnet-4-5".
• user_query (string, optional) — Original user prompt, if different from goal.
• expected_outcome (string, optional).
• metadata (object, optional) — Free-form passthrough metadata.

Returns: { trace_id, started_at }.

trustmodel_trace_step

Append a single step to the active session. Call once per reasoning step, tool call, tool result, or user-facing response.

Inputs:

• trace_id (string, required) — From trustmodel_trace_start.
• step_type (enum, required) — One of thought, think, tool_call, tool_result, observation, decision, error, human_input, response, final_answer.
• content (string, required) — Human-readable text for the step. Empty string allowed.
• tool_name (string, optional), tool_args (object, optional) — Use with tool_call.
• tool_result (string or object, optional), tool_call_success (boolean, optional) — Use with tool_result.
• model_used (string, optional), input_tokens / output_tokens (int, optional), duration_ms (int, optional), timestamp (ISO 8601, optional).

Returns: { trace_id, step_number, steps_recorded }. step_number is auto-assigned.

trustmodel_trace_finalize

Close the session, upload the trace, and auto-create the evaluation run.

Inputs:

• trace_id (string, required).
• final_response (string, optional), actual_outcome (string, optional), goal_achieved (boolean, optional), success (boolean, optional), total_duration_ms (int, optional — computed from step durations if omitted).
• goal / name / agent_framework / agent_model / expected_outcome (all optional) — Override start-time metadata if the agent learned more at runtime.

Returns (happy path): { trace_id, file_path, expires_in, step_count, evaluation_run_id, evaluation_status, evaluation_message }.

Returns (evaluate failed after successful upload): { trace_id, file_path, expires_in, step_count, evaluation_error }. You can retry evaluation without re-uploading via trustmodel_evaluate_agent({ file_path, goal, name, agent_framework }).

One-shot (pre-assembled trace)

trustmodel_upload_trace

PUT an already-built trace JSON object to cloud storage. Returns a file_path you then pass to trustmodel_evaluate_agent.

Inputs:

• trace (object, required) — Complete AgentTrace JSON.

Returns: { file_path, expires_in }.

trustmodel_evaluate_agent

Create an agentic evaluation run against a previously-uploaded trace.

Inputs:

• file_path (string, required) — From trustmodel_upload_trace or trustmodel_trace_finalize.
• goal / name / agent_framework (strings, required).
• agent_model / expected_outcome / actual_outcome (string, optional).
• goal_achieved (boolean, optional).

Returns: { evaluation_run_id, status, message }.

trustmodel_score_agent

Fetch the detail (scores, grade, summary) for an agentic evaluation run.

Inputs:

• evaluation_run_id (integer or numeric string, required).

Security

trustmodel_mcp_scan_server

Run AGT's MCP security scanner over a third-party MCP server's tool list to detect tool poisoning, typosquatting, hidden instructions, and rug-pull patterns. Static analysis — no LLM, no net