agentforge-trust-mcp
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: check_trust evaluates a server's score, evaluate_policy checks policy compliance, list_trusted searches for servers by criteria, and recommend provides natural-language recommendations. No overlap.
Naming Consistency4/5Tool names consistently use imperative verbs (check, evaluate, list, recommend) without nouns, which is a simple pattern. However, 'list_trusted' omits a noun like 'servers' for clarity, and the pattern could be slightly more descriptive, but overall consistent.
Tool Count5/5With only 4 tools, the scope is focused on trust evaluation and discovery. Each tool addresses a core need: score check, policy evaluation, search, and recommendation. No unnecessary tools, and the count feels appropriate for a trust-assessment server.
Completeness4/5The tool surface covers key operations: retrieving trust scores, checking policies, searching, and recommending. Minor gaps might include batch operations or detailed audit logs, but the core functionality for trust assessment is well-covered.
Average 4.1/5 across 4 of 4 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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?
No annotations are provided, so the description carries the full burden. It discloses key behaviors: returns up to 25 results sorted by trust score. However, it does not clarify whether the operation is read-only, requires authentication, or has side effects. A score of 3 is appropriate as it adds some context that is missing from annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, clear and front-loaded. No redundancy. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of 4 parameters, no annotations, and no output schema, the description provides essential context but still leaves gaps (e.g., what 'trust score' represents, behavior of 'required_badges'). It is adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 25% (only 'category' has a description). The description adds clarity by stating that 'category' can be omitted for all, and implicitly relates 'min_overall' to the 'minimum trust threshold'. However, it does not explain 'required_badges' or 'limit' beyond the schema defaults. With low coverage, a 3 is reasonable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the action ('search'), the resource ('AgentForge catalog'), and the scope ('servers matching a category and minimum trust threshold'). It also adds valuable context about the result set (max 25, sorted by trust score). This fully distinguishes it from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for finding servers by category and trust threshold, but does not provide explicit guidance on when to use this tool vs alternatives. It does not mention when not to use it or contrast with sibling tools.
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?
Annotations are missing, so description carries the burden. It explains return format (allowed:true/false plus individual checks) but does not disclose whether the tool modifies state, requires authentication, or has rate limits. No contradictions, but adds moderate value beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences plus a policy example. Extremely concise, no filler. Front-loaded with core purpose, then example clarifies usage. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given moderate complexity (4 parameters, nested object, no output schema), the description is complete enough: states purpose, return format, and gives example. No explanation of individual parameters beyond the example, but the schema and example together suffice.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must compensate. It does not detail parameters but provides a comprehensive example policy that covers most parameter fields intuitively. For a nested object with 5 sub-fields, the example aids understanding significantly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool checks trust policy, returns allowed:true/false with details, and provides a real example. This distinguishes it from siblings like 'recommend' or 'list_trusted'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a use-case example for gating decisions about server use for financial data, but does not explicitly mention when not to use this tool vs alternatives like check_trust.
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?
No annotations are provided, so the description carries the full burden. It mentions that the tool uses 'AgentForge semantic search + trust filter', which is helpful, but it does not disclose side effects, destructive potential, or any rate limits. A score of 3 is fair because the description adds meaningful technical context but lacks full behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences plus an example; every clause serves a purpose. It is front-loaded with the core purpose and includes a concrete example to illustrate usage. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters, no output schema, and no annotations, the description is mostly complete for a search/recommendation tool. It covers the input format (natural language), the filtering mechanism (trust filter), and provides an example. A minor gap is not specifying the output format, but for a recommendation tool, the lack of an output schema is less critical.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 33% (only 'query' has a description). The description adds semantics for the purpose of the tool but does not elaborate on parameters beyond the example. However, it provides a natural-language context that helps infer the role of 'query', and the default values for 'min_overall' and 'limit' are self-explanatory. Given the moderate coverage, the description provides added value by framing the use case.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('recommend'), a clear resource ('MCP servers'), and the filtering criterion ('by trust'). It also provides a concrete example ('validate Czech VAT IDs and convert ISDOC invoices'), which distinguishes it from siblings like check_trust or list_trusted.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly indicates when to use this tool: given a natural-language use case. However, it does not explicitly mention when not to use it or directly contrast with siblings, though the example and purpose imply it's for recommendations rather than trust checking or listing.
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?
Since no annotations are provided, the description must carry the full burden. It discloses the return structure (overall score, per-dimension breakdown, badges) and implies a read-only operation. It does not mention any destructive side effects or auth requirements, but the lack of annotations is a gap, not a contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences: first sentence states the action and outputs, second provides usage guidance. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 optional params, no output schema), the description covers the purpose, key outputs, and usage hint. It lacks details on optional parameter interactions or default behavior when multiple params are provided, but the return structure is sufficiently explained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (all three parameters have descriptions in the schema). The description adds value by listing the return fields and usage hint, but no additional parameter-level details beyond what the schema provides. Baseline 3 is elevated to 4 because the description explains what the tool returns, which helps in parameter choice.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Fetch' and the resource 'AgentForge Trust Score for an MCP server', specifying what is returned (score, breakdown, badges) and distincts from siblings like list_trusted (which lists multiple) and evaluate_policy (which evaluates policies).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description ends with 'Use before connecting to any MCP server', providing clear usage context. However, it does not explicitly mention when NOT to use it or alternative tools for other purposes, which would be helpful.
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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