TrustModel MCP Server

Server Quality Checklist

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Latest release: v0.2.0
Disambiguation5/5
Each tool serves a clear, distinct purpose: trace capture (start/step/finalize), local evaluation, governance check, and retrieval of cloud evaluation results. There is no overlapping functionality; a tool for one task cannot be easily confused with another.
Naming Consistency5/5
All tools follow a consistent 'trustmodel_verb[_modifier]' pattern using snake_case. Verbs like score, trace_start, trace_step, trace_finalize, evaluate_local, and govern are descriptive and follow a predictable convention.
Tool Count5/5
With 6 tools covering trace capture (3), evaluation (2 including retrieval), and governance (1), the count is well-scoped for an agentic evaluation and governance server. No tool is extraneous, and the set is not too sparse or bloated.
Completeness2/5
The set has notable gaps: trustmodel_trace_finalize references trustmodel_score_agent which does not exist, and trustmodel_score expects an evaluation_id from a missing trustmodel_evaluate tool. The cloud evaluation path is incomplete, leaving agents unable to retrieve scores from trace evaluations or perform cloud evaluations.
Average 4.2/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 5 commits in the last 12 weeks
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- No code scanning findings
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It describes a read operation ('Get') with no side effects mentioned, which is appropriate. However, it lacks details on idempotency, rate limits, or potential errors. The description is clear but could be more transparent about behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no redundancy: first states purpose, second covers input and output. Every word contributes value, and it is front-loaded. Very concise without being terse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple retrieval tool with one parameter and no output schema, the description is nearly complete. It explains purpose, input, and general output. It could mention possible error conditions or return format more precisely, but given the simplicity, it is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter. The tool description adds context that the evaluation_id comes from a previous trustmodel_evaluate call, but this is already in the parameter's schema description. Thus, the description adds minimal extra meaning beyond the schema, fitting the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves a trust score/detail for a previous evaluation, with specific verb 'Get' and resource 'trust score/detail'. It distinguishes from sibling tools like trustmodel_trace_* and trustmodel_evaluate_local by focusing on retrieval of past evaluation results.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use after calling trustmodel_evaluate by specifying it is for a previous evaluation created via that tool, but it does not explicitly state when to use this over alternatives nor provide exclusions. Guidance is adequate but minimal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states that the active trace session is closed and an evaluation run is auto-created, implying irreversibility. However, it does not disclose side effects like whether data can be overwritten, any rate limits, or whether finalization can be repeated. Since no annotations are provided, the description should be more thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the core action and lists subsequent steps efficiently. No wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (11 parameters, no output schema, no annotations), the description covers the main workflow and return values. It mentions how to use evaluation_run_id. However, it could be more complete by explicitly stating prerequisites (e.g., trace must be started) and explaining what 'agentic evaluation run' means. The schema handles parameter details well.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so each parameter is already explained. The tool description does not add further meaning to the parameters but provides context for return values (file_path and evaluation_run_id). Per guidelines, baseline is 3 when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verbs (close, serialize, upload, auto-create) and clearly identifies the resource (active trace session). It distinguishes itself from sibling tools like trustmodel_trace_start (opens session) and trustmodel_trace_step (logs steps), making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool should be used after starting a trace (since it closes an active session) and mentions polling with trustmodel_score_agent, but it does not provide explicit guidance on when to choose this tool over alternatives like trustmodel_evaluate_local or trustmodel_govern. Lacks explicit usage conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses auto-assigned step numbers (1-based) and default timestamp. No annotations provided, so description carries burden; however, it does not detail error handling, side effects, or behavior for invalid trace_id.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences, front-loaded with purpose. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers main usage and required parameter, but does not explain return value or error scenarios. Given high parameter count (12) and no output schema, additional context about success/failure would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all parameters have descriptions. The description adds minimal extra meaning beyond the schema (e.g., step number auto-assignment). Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'Record a single step' and resource 'active trace session'. Distinguishes from siblings (trace_start, trace_finalize) by specifying it is for step recording during an active session.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly describes when to call ('once per reasoning step, tool call, tool result, or user-facing response') and prerequisite ('Requires a trace_id from trustmodel_trace_start'). Lacks explicit when-not-to-use or alternatives, but context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It discloses that the tool runs locally, returns allowed/blocked status, policy id, and rule violations. It does not explicitly state whether the tool has side effects (e.g., logging or state changes), so it misses full transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loads the core purpose and unique benefit (local, no API key), and wastes no words. Perfectly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters and no output schema, the description covers purpose, parameter meanings, return structure, and usage context. It is self-contained and leaves no obvious gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions cover 100% of parameters, baseline is 3. The tool description reinforces the policy pack list and clarifies the 'context' parameter, but adds limited new semantic information beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Check text'), resource ('against a governance policy pack'), and result ('decide allow/block'). It also differentiates from siblings by emphasizing 'locally' and 'NO API key required'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells when to use: 'Use before letting AI output reach a user or another tool.' It also notes 'NO API key required' as a key trait. However, it does not explicitly mention when not to use or compare to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that the tool returns a trace_id and outlines the capture session lifecycle, though it does not mention rate limits, errors, or auth needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no wasted words, front-loaded with the primary action and outcome.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the overall flow and return value, but without an output schema, some details about the trace_id are implied rather than explicit. Given the complexity, it is fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline 3 is appropriate. The description does not add meaning beyond the schema's detailed parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool opens a new agent-trace capture session and returns a trace_id, distinguishing it from sibling tools like trustmodel_trace_step and trustmodel_trace_finalize.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the required sequence: use this tool to start, pass the trace_id to step and finalize, and finalize to upload. This provides clear context for when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description fully discloses behavioral traits: it runs locally, requires no API key, uses a heuristic judge, and notes that local scores are uncalibrated. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise with three focused sentences. It front-loads the purpose, then adds key details and comparison. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 params, no output schema), the description covers everything needed: what it does, return fields, and when to use alternatives. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds value by explaining that 'context' improves judging, which is not in the schema description. Params are clearly described.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it scores AI output locally across 10 dimensions and produces a TrustScore. It distinguishes itself from sibling tool 'trustmodel_evaluate' by specifying the local vs cloud nature.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool (local, no API key) and when to use the alternative (cloud, for calibrated scores). Provides clear usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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