hlido-mcp

Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Every tool has a clearly defined and distinct purpose, with descriptions explicitly guiding when to use each. Even closely related tools like find_similar_agents, find_trusted, and recommend have well-delineated use cases (semantic search vs. keyword vs. structured constraints).
Naming Consistency4/5
Most tools follow a lowercase verb_noun pattern (e.g., compare_agents, get_scorecard), but a few single-verb names like 'explain' and 'recommend' break the pattern. This is a minor deviation from full consistency.
Tool Count5/5
With 14 tools covering discovery, evaluation, submission, and subscription, the count is well-scoped for the domain of agent trust. Each tool earns its place without feeling excessive or sparse.
Completeness4/5
The tool surface covers the core workflows (discover, evaluate, compare, report, request audits) comprehensively. A minor gap is the lack of a tool to list all reviewed agents, but the existing discovery tools effectively compensate.
Average 4.4/5 across 14 of 14 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 8 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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must fully disclose behavior. It mentions the return value (current state + polling instructions) but omits side effects, idempotency, or limits (e.g., multiple subscriptions per slug). This is incomplete for a tool that implies mutability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with key caveat (Preview/Wave 3). No wasted words, but the forward-looking mention slightly reduces immediate clarity. Overall efficient.
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 two-parameter tool with no output schema, the description covers purpose, usage, return value, and parameter nuances. It provides enough context for correct invocation, though behavioral transparency is the weak link.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds value: clarifies 'slug' as a Hlido slug and explains the enum values (webhook advisory). This enriches understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool's purpose: registering interest for notification when a slug's verdict changes. It distinguishes from siblings (no other subscription tools), but the 'Preview' framing adds minor confusion.
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 states when to use it ('to register interest...'), but does not explicitly mention when not to use or give alternatives. The advisory note on webhook channel adds useful context.
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 discloses what is returned (scores, tiers, etc.) but lacks details on side effects (none expected), data freshness, error handling (e.g., invalid slugs), or response structure. For a read-only comparison, it's adequate but not comprehensive.
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: first states purpose and output, second gives usage guidelines. No wasted words, front-loaded with key information. Excellent structure.
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 no output schema, the description adequately explains return values (Laddoo score, tier, dimension scores, key claim verdicts). It specifies agent count range (2-5) and usage context. Missing details like data source recency or error handling, but sufficient for typical use.
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%, with descriptions for both the 'slugs' array and its items. The description adds context by referencing 'Hlido-reviewed agents' and example usage, but does not add substantial meaning beyond the schema. 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?
The description clearly states the tool performs a head-to-head comparison of 2-5 Hlido-reviewed agents, listing specific return values (Laddoo score, tier, dimension scores, key claim verdicts). It distinguishes itself from siblings like find_trusted and recommend by specifying the stage at which it's used (after shortlisting).
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?
Explicitly provides when-to-use guidance: 'Use this once you've shortlisted candidates...and need a direct comparison.' Names alternative tools (find_trusted, find_similar_agents, recommend) as predecessors. This is model behavior for usage context.
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 full burden. It discloses the tool is a read operation ('Fetch'), returns claims, verdicts, evidence quotes, source surface, and for CLI/API tests includes command, exit_code, and duration. The term 'sanitized' adds context. It does not mention permissions or side effects, but for a fetch operation this is adequate.
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 sentences: first sentence states purpose and output; second gives version and use case. No redundant information. Every sentence adds value. Front-loaded with key information.
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 tool with one parameter and no output schema, the description covers the main return fields and use case. It lacks error conditions or invalid slug handling, but is largely complete. The version note is a nice touch.
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 schema already fully describes the single parameter 'slug' with explanation and examples. The description does not add additional meaning beyond the schema. With 100% schema coverage, baseline is 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 fetches a 'full sanitized claim-vs-evidence scorecard' for a single agent. The verb 'fetch' and resource 'scorecard' are specific. It distinguishes from siblings like 'compare_agents' by explicitly stating the use case 'for agent-to-agent pre-flight evaluation' and focusing on a single agent.
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 provides a clear use case ('agent-to-agent pre-flight evaluation'). While it does not explicitly mention when not to use or list alternatives, the sibling tools in context signal different purposes. The guidance is clear but lacks exclusions.
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?
Without annotations, the description carries full burden. It mentions output format but does not disclose side effects, safety implications, or behavioral traits like idempotency or rate limits. It implies read-only but doesn't explicitly state.
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 sentences, front-loaded with purpose, efficient and no fluff.
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 one nested parameter, no output schema, and 13 siblings, the description covers what, why, and when. Minor gaps: no explanation of ranking or why_match format, but sufficient for selection and invocation.
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 description lists parameter names but adds little beyond the schema: it clarifies 'any combination' optionality. With schema description coverage at 0% (context signal), it should compensate more by explaining parameter roles and formats.
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 is a constraint-driven recommendation across Hlido's reviewed agents, lists input parameters, and describes output as ranked candidates with why_match. It distinguishes itself from sibling tool trust_check.
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?
Explicitly instructs to use when buyer constraints exist and want a filtered shortlist instead of one-by-one trust_check calls, providing clear guidance on when to use and an alternative.
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 full burden. It discloses that reports are processed daily and that a re-test sub-agent is triggered. This gives useful behavioral context, though it omits any destructive hints, auth needs, or rate limits.
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 three clear, front-loaded sentences. The first states purpose, the second gives usage condition, the third explains processing. No redundant information.
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 purpose, usage, and processing behavior. However, it lacks information about return values (e.g., confirmation or error handling) since no output schema is present. This is a minor gap for a reporting tool.
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 the schema already documents all four parameters. The description only adds minor context (e.g., issue type enumeration and example of detail), but does not significantly enhance understanding 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 explicitly states the verb 'Report' and resource 'Hlido review' with specific issue types (stale info, wrong verdict, missing claim, broken link). It clearly distinguishes from siblings like get_scorecard and trust_check which are query tools.
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 says 'Use when calling get_scorecard or trust_check returns data you can prove is incorrect,' providing a clear trigger condition. It implies when not to use (without proof) but does not list alternative tools for other scenarios.
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?
Given no annotations, the description fully carries the burden of transparency. It accurately states the tool returns structured data with claims, verdicts, evidence snippets, and synthesis. There are no hidden side effects or destructive actions implied, but it could mention if the tool is purely read-only or caches data, which would earn a 5.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured, with no unnecessary words. It uses clear, functional language. However, it could be slightly more structured (e.g., bullet or numbered list), so it does not quite earn a 5.
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 only 2 parameters and no output schema, the description provides thorough context: it explains the return structure (claims, verdicts, evidence snippets, synthesis), how to use the optional filter, and hints at discoverability of dimension values. This is complete and covers the agent's needs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. The description adds value beyond the schema by explaining the purpose of the dimension filter and providing a best practice (run without dimension first). This extra guidance justifies a 4.
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 identifies the tool's purpose: providing structured natural-language explanations of why a Hlido-reviewed agent has its current score, pulling claim-by-claim evidence. It distinguishes the tool from siblings like get_scorecard and verify_claim by focusing on explanation rather than raw scores or verification.
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 gives clear usage context: pass an optional dimension to filter, omit for full picture. It even advises running without dimension first to discover supported_dimensions. However, it does not explicitly state when not to use this tool or mention alternatives, so it lacks direct exclusions.
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 describes the tool as a semantic search (read-only) returning results with specific attributes. It does not mention any destructive behavior or side effects, but the nature of the tool implies safety. The description adds value beyond structured fields by explaining the ranking mechanism and output fields.
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 sentences with a clear example and usage note. No redundant words; purpose and guidance are front-loaded. Every sentence adds value.
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 no output schema, description adequately explains what the output contains (Laddoo score, evidence_tier, review URL). It covers purpose, usage, and return fields. Could mention pagination or error handling, but not required for basic usage. Score 4 as it is mostly 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% (all three parameters described). The description does not add additional meaning beyond the schema; it only provides context for usage. Baseline score of 3 is appropriate as description does not need to compensate.
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 it performs semantic search over Hlido's review corpus, returns ranked agents with Laddoo score, evidence_tier, review URL. It distinguishes from sibling 'find_trusted' by noting it's better than substring matching. Verb 'find' is clear, and the example task description clarifies scope.
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?
Explicitly states 'Use this when you have a task in mind and want Hlido's recommendation' and contrasts with 'find_trusted' for substring matching. This provides clear guidance on when to use this tool vs alternative.
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 fully bears the burden. It clearly explains what the tool returns and possible response statuses, and it implies a read-only, non-destructive operation. It does not disclose rate limits or auth requirements, but the behavior is well-described.
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 sentences with no wasted words. The first sentence lists outputs, the second explains special statuses, and the third gives usage context. Perfectly front-loaded and efficient.
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?
Despite lacking an output schema, the description thoroughly explains return values and statuses. For a 2-parameter tool, this provides sufficient context for an agent to understand invocation outcomes without external documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds value by providing an example slug ('aider', 'opencode') and clarifying that omitting spec_version fetches the latest. This goes beyond the schema's default specification.
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 fetches behavioral evaluation traces for Hlido-reviewed agents, listing specific return fields (per-task pass/fail, adapter, behavioral tier, signed trace link) and statuses (not_yet_bench_tested, not_testable). This specific verb+resource combination distinguishes it from sibling tools like get_scorecard or verify_claim.
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 includes an explicit usage suggestion: 'Use this when you need evidence that an agent's coding/task behaviour has been independently verified beyond marketing claims.' It does not explicitly state when not to use or name alternatives, but the context is clear enough for an agent to decide.
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?
Describes return value (confirmation with tracking reference), queuing behavior, and public scorecard. Lacks details on authorization or potential side effects, but adequate given no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two well-structured sentences: first sentence for purpose, second for usage and return info. 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?
Covers all necessary context: return value, queuing behavior, mention of scorecard, and alternative tool. No output schema needed.
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 covers all parameters 100%, description adds no new meaning beyond schema. Baseline score of 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 specifies the action ('Nominate a new AI agent'), the resource ('Hlido to review'), and distinguishes from siblings by referencing trust_check and request_quick_audit.
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?
Explicitly states when to use ('when an agent isn't in Hlido's corpus yet...') and provides an alternative tool (request_quick_audit) with a clear reason.
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 full disclosure burden. It describes the type of data (real failures and self-reported incidents), severity, evidence, vendor-response status, and filters. It does not mention rate limits or authentication, but for a read-only fetch, the disclosure is adequate.
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 sentences with no wasted words. First sentence states purpose and content, second lists filter options, third provides usage guidance. Front-loaded and efficient.
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 no annotations and no output schema, the description covers purpose, data content, filter parameters, and usage expectations. It hints at return fields (severity, evidence, vendor-response status). Missing explicit return type but sufficient for a fetch tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for each parameter. The description adds meaning beyond the schema by explaining the nature of the incidents and the context of filters (e.g., 'published incidents'). It does not repeat schema text verbatim, enhancing understanding.
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 ('Fetch published incidents') and a clear resource ('Hlido's NTSB-style failure registry'), distinguishing it from sibling tools that focus on comparisons, explanations, or recommendations. It uniquely describes the content (real observed failures and self-reported process incidents).
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?
Explicitly advises using this tool before delegating to an agent to check for known failures, and clarifies that an empty list means no published incidents, not a guarantee of reliability. This provides clear when-to-use context and manages expectations.
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?
With no annotations, the description fully discloses behavior: returns verdict types (PASS/FAIL/PARTIAL/UNKNOWN), includes evidence snippet and source, and returns null when untested with an explanation of what that means. This builds trust and sets expectations clearly.
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 sentences, no fluff, purpose first, then behavior and usage cue. Every word earns its place.
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 main behavior, return types, and edge case (untested claim). Without an output schema, it sufficiently explains what the agent can expect. Could mention idempotency or performance, but not essential for this simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear parameter descriptions. The description adds value by specifying that claims should be a 'specific marketing or capability claim' and that verification uses 'Hlido's independent testing', which enriches the context beyond the schema alone.
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's purpose: to fact-check a specific marketing or capability claim against Hlido's independent testing. It uses a strong verb-resource pair and distinguishes from siblings like 'trust_check' or 'get_scorecard' by focusing on one claim at a time.
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 the tool ('validate a vendor's specific promise before you rely on it') and hints at scope (one specific claim). It does not explicitly mention when not to use or list alternatives, but the context is clear enough.
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 discloses the return structure (agents with Laddoo score, tier, review URL), ranking by trust, and minimum tier filtering. This adequately informs an agent about behavior, though it could mention ordering direction or pagination.
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 sentences, no filler. First sentence states core purpose and output; second sentence gives usage switch. Information is front-loaded and every word earns its place.
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, no output schema, and no annotations, the description covers purpose, input semantics, output structure, and sibling distinctions comprehensively. An agent has enough information to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, providing good baseline. The description adds context beyond schema: 'ranked by trust' and 'matching a free-text need' complement the need parameter. The min_tier default and enumeration are clear. Slight extra value justifies 4.
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 verb 'discover' (find) and the resource 'Hlido-reviewed agents' matched by free-text need. It explicitly differentiates from siblings find_similar_agents (semantic task-matching) and recommend (structured filters), making the 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 Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on when to use this tool ('keyword/need-based discovery') and when to prefer alternatives ('semantic task-matching prefer find_similar_agents, structured filters prefer recommend'). This helps an agent select the correct tool.
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 fully carries the burden of behavioral disclosure. It describes the return values (trust score, tier, verdict, claim summary, incidents) and the no_review_found edge case. It does not mention any side effects, auth requirements, or rate limits, but for a read-only query tool this is adequate. Slightly more detail on the verdict tailoring for use_case would improve 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 concise and well-structured. It front-loads the core purpose, then provides usage guidance, edge-case handling, and follow-up recommendations. Every sentence adds value without repetition, making it easy for an AI agent to parse.
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 lack of annotations and output schema, the description is remarkably complete. It covers input parameters, return values, an edge case, and coordination with sibling tools. The agent can reliably understand when and how to use this tool without additional documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. However, the description adds significant value beyond the schema: it explains that 'agent_or_url' can be either a slug or a URL, and that URLs are matched to the closest reviewed agent. It also clarifies that 'use_case' is optional and tailors the verdict. This additional context enhances understanding of parameter behavior.
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's purpose: to check if a specific AI agent is trustworthy using Hlido's trust data. It specifies the input (slug or URL) and the output (trust score, tier, verdict, claim summary, incidents). It distinguishes itself from siblings by positioning itself as the first call before using other tools like get_scorecard or request_quick_audit.
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?
Explicitly states when to use: 'before delegating to, installing, or relying on another agent'. Provides clear guidance on alternatives: if no_review_found, call request_quick_audit; for full claim-by-claim evidence, use get_scorecard. This gives the agent a decision tree for trust verification.
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?
Without annotations, the description fully discloses behavioral traits: returns a future scorecard URL + ETA, rate limits (5/day anonymous, 50/day identified), and turnaround time (~24h or sooner). No contradictions or omissions.
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 concise with two front-loaded sentences covering purpose and usage, followed by additional details. Every sentence adds value, no fluff.
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 has 4 parameters (1 required) and no output schema, the description explains the return value (scorecard URL + ETA), rate limits, and timing constraints. It is fully complete for agent use.
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%, and the description adds extra meaning: explains the purpose of optional parameters (why helps prioritize, requester for rate-limiting/follow-up). This adds value beyond the schema definitions.
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 explicitly states the tool's purpose: 'Request that Hlido audit a NEW AI agent that has no review yet.' It specifies the verb (request audit), resource (new agent), and condition (no review yet), clearly distinguishing it from siblings like trust_check and get_scorecard.
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?
Directly states when to use: 'Use this when trust_check or get_scorecard returns no_review_found and you need a verdict before delegating to the unknown agent.' Provides explicit context and mentions alternative tools, giving clear guidance.
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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