FindAgent
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have clearly separate purposes: one searches the agent catalog, the other provides an overview of the service itself. There is no overlap or ambiguity in what each tool is for.
Naming Consistency5/5Both tool names follow the same verb_noun snake_case pattern: search_agents and get_overview. The naming is consistent, predictable, and readable.
Tool Count3/5With only two tools, the surface feels thin for a public agent catalog. The tools are useful, but the low count makes the server feel minimal rather than fully featured.
Completeness3/5The core search capability is present, but there is no obvious way to retrieve a specific agent by slug, list agents without a query, or browse by category. The overview endpoint is meta-information rather than a domain operation, leaving some discovery workflows incomplete.
Average 4.2/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 10 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
Tools from this server were used 4 times in the last 30 days.
This repository includes a glama.json configuration file.
This server has been verified by its author.
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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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description properly discloses that the operation is read-only and requires no auth, which covers the main safety and access profile. It also states the returned fields. It doesn't mention result limits or ordering, but the core behavioral traits are explicit.
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, front-loaded with the primary action and resource, followed by return fields and access traits. No wasted words; every sentence adds useful information.
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?
For a single-parameter search tool with no output schema, the description covers purpose, return contents, and access requirements. It could additionally clarify result limits or query syntax, but the schema example mitigates that, so it is nearly 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?
The input schema fully documents the query parameter with examples, and the tool description adds little beyond confirming that matching agents are returned. Since schema coverage is 100%, a baseline of 3 is appropriate; no extra semantics are needed.
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 ('Search') and a specific resource (FindAgent's public catalog of vetted MCP 'doer' agents), names categories, and lists return fields. It clearly conveys what the tool does and differentiates from an overview tool.
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: use when you need to find agents by search terms. It doesn't explicitly compare with get_overview or state when not to use this tool, but the read-only search context is clear. Lacks explicit alternatives.
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?
No annotations are provided, so the description carries the full burden. It explicitly discloses that the operation is read-only, requires no auth, and returns machine-readable output. This is substantial behavioral context for a simple no-parameter tool, though it does not specify the exact response structure.
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 a single, well-structured sentence that front-loads the action ('Get') and the object ('machine-readable overview'), then adds the content scope and behavioral notes. Every clause earns its place with no redundant or filler wording.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, read-only overview tool, the description covers purpose, content, output format, and access requirements. No output schema exists, but the description's 'machine-readable overview' adequately sets expectations. There is no missing information an agent would need to invoke it correctly.
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?
The tool has zero parameters, so there is nothing to document. The description appropriately avoids inventing parameter details and instead focuses on the tool's output. The baseline of 4 applies because with no parameters, no semantic clarification is needed.
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 names a specific verb ('Get'), a clear resource ('overview of FindAgent'), and the content scope ('what it is, how to connect, and the key public routes'). This clearly distinguishes it from the sibling search_agents, which implies a search behavior rather than an overview.
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 its use case: an agent should call this when it needs a machine-readable overview of FindAgent, and search_agents when it needs to find agents. However, it does not explicitly state when to prefer this over search_agents or mention any exclusion criteria, leaving the routing decision to inference.
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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