MyMCPTools

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark it as read-only. The description adds substantial behavioral context by detailing exactly what metrics are returned (handshake latency percentiles, tool counts, probe freshness), which is beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with zero wasted words. The first sentence lists the key data points, and the second provides a clear tagline. Perfectly front-loaded and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description compensates by enumerating the return contents comprehensively. The tool is simple (aggregate stats, no parameters), and the description fully covers what an agent needs to know.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has no parameters, so the input schema is fully covered. The description does not need to add parameter information, and it does not repeat schema details. Baseline 4 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool aggregates health stats for the whole MCP population and lists specific metrics (verdict breakdown, serving share, etc.). It distinguishes itself from sibling tools like get_mcp_server or get_server_history which operate on individual servers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly frames the output as a headline number for overall health, implying when to use it. It does not explicitly list alternatives or conditions to avoid, but the purpose is clear enough for an agent to make a correct selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true. The description adds context about return fields (probe verdict, freshness signal) but does not disclose behavioral traits like caching, latency, or rate limits. 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence listing many items, which is information-dense but not excessively wordy. Could be slightly improved with line breaks, but overall efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description helpfully enumerates return fields (description, author, install command, etc.). It lacks details on error handling or edge cases, but for a simple lookup tool it is fairly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers the sole parameter 'slug' with a good example. The description mentions 'by slug' but adds no new semantics beyond what the schema provides, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves a full catalog entry for one MCP server by slug, listing specific return fields. This distinguishes it from sibling tools like search_mcp_servers that return lists or other tools with different data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when you have a specific slug and want detailed info, but it does not explicitly contrast with alternative tools or state when not to use it. No exclusion criteria or context for selection are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, so the description's addition of 'trailing probe history' and 'raw probe points' provides useful context about the data returned without contradicting annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler: first sentence states purpose and context, second lists output structure. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description covers return types. All 3 parameters are documented. Context about the 'badge' provides real-world relevance. Minor gap: no mention of rate limits or data freshness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so all parameters are already described in the schema. The description adds no additional input semantics beyond the schema's own descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns 'trailing probe history for one server plus a daily uptime sparkline', specifying the resource (server uptime history) and action (get). It differentiates from siblings like get_server_status (current) and list_server_incidents (events) by focusing on historical uptime data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for checking uptime history ('the reliability signal behind the badge') and mentions output types, but does not explicitly state when to use this tool versus alternatives or provide exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds significant detail beyond the readOnlyHint annotation, enumerating the verdict states, returned fields, and the conditional behavior when slug is omitted. This fully discloses the tool's output and two operational modes.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is only two sentences, front-loading the action and listing outputs concisely, then providing the usage hint. No extraneous wording.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one optional parameter, no output schema, readOnly annotations), the description fully covers output fields and the two request modes. It is complete for an agent to understand and invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the parameter description already states 'Omit for the catalog-wide verdict rollup.' The tool description repeats this, adding no new semantic information beyond what the schema provides. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves live status for a single server, listing specific fields like verdict, tool count, and latency. It also distinguishes from siblings by noting that omitting the slug yields a catalog-wide verdict rollup, which is unique among sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly explains when to omit the slug for a catalog-wide summary, giving direct usage guidance. However, it does not contrast with specific sibling tools or state when not to use, which would elevate it to a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true; description adds that it returns all categories and integrations with counts, which is useful behavioral context beyond the annotation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first is clear purpose, second adds valuable usage context. No extraneous words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Adequate for a zero-parameter list tool: describes return content and how to use results. Could mention sorting or format, but not necessary.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters, schema coverage is 100%. Description does not need to add param info. Baseline 4 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it lists every category and client integration with server counts, and distinguishes from siblings by noting the slugs are for filtering on search_mcp_servers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit context: slugs returned are valid filter values for search_mcp_servers. Does not explicitly state when not to use, but usage is clear from sibling differentiation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations already declare readOnlyHint=true, so the agent knows it's safe. The description adds valuable context: the tool returns drift events between successive probes, ordered newest first, and describes the content of events. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that front-loads the core functionality ('Tool-schema and protocol-version drift events'), provides a usage purpose, and includes ordering. Every part is essential and there is no waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the low complexity (no output schema, 2 optional parameters with clear descriptions), the description covers the main idea adequately: it explains what the tool returns and its purpose. It does not specify response format or pagination, but the implied list of events is sufficient for basic understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage and each parameter is well-described (slug: restrict to one server, limit: max events with default and bounds). The description adds ordering info ('newest first') but does not add meaning beyond what the schema already provides for the parameters themselves.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists drift events between successive probes, detailing what each event includes (added, removed, or changed tools and when). It distinguishes itself from sibling tools like get_server_history by focusing specifically on schema and protocol-version drift, and explicitly mentions the use case of spotting breaking changes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a clear use case: 'Use this to spot breaking changes in a server you depend on.' It does not explicitly mention when not to use it or list alternative tools, but the given context is sufficient for an agent to decide when to invoke this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only and non-open world. The description adds behavioral details: incidents are reconstructed, ordered newest first, include start/end/duration/reason. No contradictions; adds useful context 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences. First sentence defines output structure and ordering. Second explains filtering. Every word adds value; no redundancy. Front-loaded with purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description covers incident structure (start, end, duration, failure reason) and ordering. Filtering options are explained. For a list tool with 3 optional params, this is complete and informative.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with good descriptions for all three parameters. The description restates filtering options but does not add significant new semantics beyond the schema. Baseline 3 is appropriate as the schema already provides clear parameter meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists 'Reconstructed outage incidents' and specifies they are 'contiguous runs of failed probes' across the catalog. It distinguishes from sibling tools by focusing on incidents rather than stats or server status. Filtering options differentiate it further.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use the tool by specifying filtering by server slug or status ('ongoing'). It implies usage for examining outage incidents but does not explicitly state when not to use it or compare to siblings. Clear enough for most contexts.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, so the description does not need to reiterate safety. The description adds value by explaining that results carry a 'live verdict' and that only_verified filters servers based on the latest MCP handshake probe, giving the agent clear behavioral expectations beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long. The first sentence clearly states the tool's primary purpose. The second sentence lists filters and highlights a key parameter (only_verified). No fluff, fully front-loaded, every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 7 parameters and no output schema, the description covers all filter types and explains the live verdict behavior. It could mention result pagination or that results include basic server info, but the description is functionally complete for an agent to understand what to expect. Slight room for improvement.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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. The description adds extra context for the only_verified parameter, explaining its purpose ('restrict to servers that answered a live MCP handshake on the most recent probe'). For other parameters, it lists them without repeating schema info, which is sufficient given the schema already fully documents them.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Search the MyMCPTools catalog of Model Context Protocol servers.' It lists specific filter types (free-text query, category slug, integration slug, install type, official status, only_verified), distinguishing it from sibling tools like get_catalog_stats or get_mcp_server.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description tells when to use this tool (when you need to search or filter servers) and lists supported filter parameters. While it doesn't explicitly state when not to use it or name alternatives, the sibling tool names imply other tools for specific tasks (e.g., get_mcp_server for a single server).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.