DeployIt Product Expert

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

With no annotations provided, the description carries the full burden and delivers richly: it explains the tool searches source code, takes 20-60 seconds, has timeout/background-retry behavior, caching semantics, output verification statuses, read-only guarantees, quota limits, and rate limits. This is exceptional transparency beyond any structured 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?

Though long, every sentence earns its place. The description is organized into labeled sections, front-loads the core purpose, and packs high-value operational detail without filler. The length is justified by the tool's complexity and lack of annotations/output schema.

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 description covers what the tool does, when to use it, timing expectations, timeout behavior, how to read the answer, verification status meanings, safety limits, quota, and rate limits. With no output schema and no annotations, this is a complete operational guide for correct invocation and interpretation.

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 the baseline is 3. The description adds valuable parameter-level meaning: identical 'question' wording hits the cache, retries must be verbatim, 'context' disables caching, and 'depth: thorough' is suggested when status is unknown. This goes 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?

The description uses a specific verb and resource: 'Ask a question about deployit' and states the outcome ('answer verified against its live source code and git history'). It enumerates the topics it covers, but it does not explicitly differentiate from siblings like get_recent_changes, so it stops short of full 5-level distinction.

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?

It explicitly says 'Use it for anything about product behavior, features, settings, limits, error messages, or recent changes,' giving clear context for when to invoke the tool. However, it does not say when not to use it or compare it to alternate tools, so it lacks a true exclusion/alternative statement.

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 behavioral burden and does it well: it discloses no AI processing, sub-second response time, zero cost, and no quota consumption. It doesn't detail the exact output structure, but for a simple changelog list the disclosed traits cover the most decision-relevant 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 three tight sentences, each earning its place: the first defines the resource, the second clarifies performance/cost behavior, and the third gives routing guidance. It is front-loaded with the core purpose and contains no filler.

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 tool with one optional, well-documented parameter and no output schema, the description covers purpose, behavior, and usage boundaries. The return value is inherently implied by 'list of recent changes,' and the sibling relationship is handled explicitly. Nothing essential is missing for safe invocation.

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%, and the only parameter, 'days', is already fully documented in the schema with default and max values. The description adds no additional parameter-specific guidance, so the schema does the heavy lifting and the baseline of 3 applies.

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 specifies exactly what the tool returns — an instant list of recent changes to 'deployit' derived from commits as a semantic changelog. It clearly distinguishes this from the sibling ask_product_expert by framing it as the lightweight, non-AI option for 'what changed' questions.

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?

Usage guidance is explicit and prescriptive: 'Use this FIRST for ... questions' and 'escalate to ask_product_expert only when you need an investigated answer.' This gives the agent a clear decision rule for choosing between the two sibling tools.

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, the description carries the disclosure burden and does well: it promises 'Instant (no AI)', states the kind of information returned, and reveals a critical caveat about stale or building indexes. It doesn't detail output structure or failure behavior, but for a zero-parameter info tool this is a solid disclosure.

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: the first defines the resource and content, the second gives timing, usage, and a conditional instruction. Information is front-loaded and every clause 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?

For a no-parameter, no-output-schema introspective tool, the description covers what the server answers for, which repositories back it, freshness, latency, and when to use it. No important calling context is 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?

The tool has no parameters, so there is nothing to document; the description correctly avoids inventing parameter details. This matches the baseline for a zero-parameter tool.

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 names a specific action (describe this MCP server) and a concrete resource scope (product, repositories, index freshness), so an agent can tell what it does. It implicitly contrasts with deep-answer tools ('instead of trusting deep answers') but does not explicitly name sibling alternatives, so it stops just short of full differentiation.

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?

It gives explicit guidance to call this tool once at the start of a session to confirm the correct product, and explains what to do if the index is stale or building. It lacks a named alternative like ask_product_expert for deeper questions, but the context signals make that inferable.

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