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

Annotations are absent, so description carries the burden. It discloses 'Read-only disk read; errors if not found' and the lookup fallback order, giving agents a clear safety profile and error expectation.

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 organized into Behavior, Usage Guidelines, and Parameters sections in four concise sentences. Every sentence adds information and the main purpose is front-loaded.

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?

Despite no annotations and no output schema, the description covers what the tool returns (full markdown instructions and manifest), its read-only behavior, failure mode, and selection criteria. For a one-parameter get tool, nothing essential is missing, though a bit more detail about the return structure could help.

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 already documents the id parameter with case-insensitive lookup semantics at 100% coverage, but the description adds the precedence rule that exact skill ID is matched first, then falls back to case-insensitive name. This is meaningful extra meaning beyond the schema.

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?

Description uses specific verb 'Retrieve' with resource 'full markdown instructions and manifest for a skill by ID or name.' It distinguishes itself from route_skill by noting that route_skill is for searching by prompt intent, so an agent can tell them apart.

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?

Explicitly states when to use: when skill ID or name is already known (e.g. from route_skill or list_skills). It also states the alternative: 'Use route_skill to search by prompt intent,' providing clear selection logic.

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?

No annotations are provided, so the description carries full responsibility. It clearly states 'Read-only, sub-millisecond query with zero side effects,' which fully discloses behavioral characteristics with 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?

Organized into clear sections (Behavior, Usage Guidelines, Parameters) with no redundant wording. Every sentence adds meaningful information, achieving high density without bloat.

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?

The tool is simple (list/filter, no output schema needed). The description covers purpose, behavior, usage, parameter semantics, and alternatives comprehensively, leaving no relevant gaps for an agent to invoke it correctly.

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 baseline is 3, but the description adds valuable context: 'combine as an AND filter' and 'Returns all skills when omitted,' which clarifies parameter interaction and default behavior beyond the individual schema 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?

States a specific verb ('list' and 'filter') with a clear resource ('locally indexed agent skills from SQLite'). Explicitly differentiates from siblings by naming alternative tools and their purposes (scan_skills, route_skill, get_skill).

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 when-to-use guidance ('Use to browse available skills without a prompt') and explicitly names alternatives with their appropriate contexts, leaving no ambiguity about 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?

No annotations are provided, so the description carries the full behavioral burden. It discloses that this is a 'Local SQLite update', that it is 'Idempotent', and gives a performance expectation of '<5ms'. This goes beyond a generic 'records feedback' and provides meaningful side-effect and safety context, though it doesn't describe error/failure behavior.

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 compact, front-loaded with purpose, and logically structured with labeled Behavior, Usage Guidelines, and Parameters sections. Every sentence contributes operational value, and the key usage condition is immediately actionable.

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?

For a two-parameter, no-output-schema, feedback-recording tool, the description is complete. It covers purpose, when to invoke it, side-effect characteristics (idempotent local update), and parameter meanings in context. No critical operational detail appears to be missing.

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 restates 'query is the routed prompt; chosenSkillId is the correct skill identifier', but this adds minimal semantic value beyond the schema. It reinforces the routing context but does not introduce new format, constraints, or examples.

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?

States a specific verb and resource: 'Record user routing corrections to refine scoring weights and learn prompt-to-skill synonyms.' This clearly differentiates it from siblings like route_skill (routing), get_skill/list_skills (retrieval), and scan_skills (scanning). The resource and high-level effect are unambiguous.

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?

Explicitly states when to use the tool: 'Use after route_skill when a user approves or corrects a skill route.' This gives a clear trigger condition and a sequencing relationship to its primary sibling. It leaves no ambiguity about the intended invocation context.

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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure, and it delivers: read-only execution, local CPU operation, sub-20ms latency, and zero LLM context tokens. These are concrete, useful behavioral traits that an agent needs to decide whether to call this tool.

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 compact, uses labeled sections for behavior, usage guidelines, and parameters, and contains no filler. Every sentence contributes to either tool selection, invocation, or safety/performance understanding.

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?

For a routing tool with no output schema, the description covers the core purpose, behavior, alternatives, and parameter semantics. An agent has enough context to decide when to use it and how to call it correctly.

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 summarizes each parameter accurately, but adds little beyond what the schema already states about prompt, topK, host, and explain.

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 states a clear verb and resource: route natural language prompts to matching agent skills, with the matching mechanism specified as hybrid BM25 and dense embeddings. It also distinguishes itself from siblings by explicitly naming list_skills, get_skill, and scan_skills as alternatives with different purposes.

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 labels route_skill as the primary entry point and gives concrete conditions for using each sibling tool: list_skills for browsing without a prompt, get_skill for known IDs, and scan_skills for refreshing the index. This gives an agent clear decision criteria for tool 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?

With no annotations provided, the description carries the full burden and does well: it discloses the in-place SQLite index update, a <100ms performance trait, and explicitly states it is read-only on source skill files. It could add more on failure modes or permissions, but the core behavioral traits are covered.

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 compact, uses clear section labels (Behavior, Usage Guidelines, Parameters), and leads with the main purpose. Every sentence adds useful information, and there is no fluff or repetition.

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?

For a simple one-parameter maintenance tool with no output schema, the description covers purpose, behavior, usage, and parameter semantics adequately. It does not describe the return value or success/failure feedback, but given the tool's simplicity and the lack of a meaningful output schema, the missing detail is minor.

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 schema already fully documents the workspace parameter. The description adds a small reminder that workspace is an absolute directory and clarifies the omission behavior, matching what the schema already states. 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 states a specific action ('Scan filesystem directories', 'rebuild the local SQLite index') and a clear resource (skill files across detected AI coding tools). It distinguishes itself from sibling query tools like route_skill and list_skills, so an agent understands its maintenance role.

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?

Explicitly says when to use it ('after adding or editing skill files') and names alternatives for querying ('Use route_skill or list_skills'). This gives the agent clear routing criteria without inference.

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