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

No annotations are present, so the description carries the full burden. It clearly frames the tool as an analysis step 'before making the change' and discloses its output behavior, which strongly implies it does not perform the modification itself. However, it never explicitly states whether it is strictly read-only or whether it has side effects such as caching or workspace state changes, leaving a small transparency gap.

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 short labeled sections — Purpose, When to use, Best practice, Avoid — and every sentence earns its place. It front-loads the core purpose and outputs before giving procedural guidance, and it avoids redundant 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 three-parameter tool with no output schema or annotations, the description is complete: it explains purpose, outputs, invocation best practices, and exclusions. It even routes ambiguous cases to the correct sibling tool, pce_query, which gives an agent everything needed to invoke pce_impact 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 coverage is 100%, so the schema already documents all three parameters. The description adds value beyond the schema by explaining that target should be explicit, that file can speed up resolution, and by recommending concrete change-framing examples like 'modify field X' or 'change function signature of Y'. It does not add much beyond the schema for change_type, but the extra guidance is meaningful.

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 verb and resource ('analyzing the impact boundary of a known change target') and enumerates concrete outputs: direct call sites, direct consumers, main propagation chains, risks, and suggested modification order. It also differentiates itself from siblings by explicitly naming pce_query as the discovery tool, so an agent can distinguish impact analysis from query/search.

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 when-to-use conditions ('already know which symbol, field, interface contract, or file to change') and explicit avoid-conditions ('target is still ambiguous... use pce_query first'). It also clarifies that impact is unnecessary for viewing local implementations, leaving no ambiguity about when this tool should be selected.

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 does disclose that the tool builds an index and navigation context, that it establishes context rather than answering code questions, and that it must succeed before dependent tools are used. It does not, however, describe side effects on disk, persistence, idempotency, or what success/failure returns, which would make the behavioral profile more complete.

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 well structured under Purpose, When to use, Best practice, and Avoid, with each section front-loaded and every sentence adding operational value. It is sufficiently concise and avoids redundancy while covering usage, sequencing, and scope.

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 one-parameter initialization tool with no output schema and no annotations, the description covers the essential context: what it does, when to call it, its prerequisite status, and its relationship to siblings. It is slightly incomplete in not stating what a successful initialization returns or whether any external state is modified, but overall it is strong enough for an agent 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%, and the only parameter, project_path, is already described as 'Absolute path to the target project root.' The description adds no additional parameter-specific guidance beyond restating the notion of a 'target project,' so the baseline score of 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 ('Bind a target project and initialize the PCE runtime') and the resource affected, and it explains the purpose by saying it builds 'the index and navigation context required by query / impact / sync.' It also distinguishes itself from siblings by explicitly warning not to use it as a code query tool.

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 gives explicit conditions for use: call when entering a new project or when PCE is first needed, and do not call pce_query, pce_impact, pce_sync, or edit tools before pce_init succeeds. It also provides best practice context, saying it is typically called once per session and only again when switching projects or retrying after failure.

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?

There are no annotations, so the description carries the behavioral burden. It explains the tool is exploratory/navigational, accepts natural-language questions, and can return structured navigation artifacts like file:line references, name_path, candidate file lists, call chain summaries, or grouped results. It does not explicitly state whether the operation is side-effect-free, but the read-only intent is strongly implied.

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 longer than average but uses labeled sections (Purpose, When to use, Best practice, Avoid) that make it scannable and front-loaded. A little redundancy exists between 'Best suited for...' and 'especially appropriate for...', but every paragraph adds actionable guidance.

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 what the tool can return and what kinds of questions it answers. It also covers exclusions and sibling routing. Nothing essential for an agent to decide whether to invoke it 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 schema only describes 'query' as a natural-language question. The description adds substantial semantics: prefer natural language over exact identifiers, example question, what output styles can be requested, and appropriate question types. This goes well 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 names a specific verb ('query'), resource ('codebase'), and a set of concrete use cases (entry points, module responsibilities, call chains, narrowing search scope). It explicitly distinguishes itself from pce_impact, 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?

Contains explicit when-to-use and when-not-to-use guidance: use first when target location is unclear; avoid when exact file/identifier is known; switch to pce_impact for change-impact analysis. It even warns against manually traversing directories before trying 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?

No annotations are provided, so the description carries the burden of behavioral disclosure. It does disclose that the tool is diagnostic and read-only in nature, and it lists what information is returned. However, it does not describe the output format, whether the tool performs any side effects, or what happens if the service is unavailable. The diagnostic framing is useful but the behavioral detail is thin.

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 and well-structured with clear labeled sections: Purpose, When to use, Best practice, and Avoid. Every sentence earns its place, and the most important information (what the tool returns) is front-loaded. There is no redundancy or 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 zero-parameter diagnostic tool with no output schema, the description covers the essential information an agent needs: what the tool does, when to call it, and when not to call it. The only gap is the lack of detail about the exact shape or format of the returned status, but since there is no output schema, a bit more specificity about return values would make it fully 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 tool has zero parameters, so there is no parameter semantics burden on the description. The schema is trivially complete (100% coverage with an empty properties object). The description adds value by explaining what the returned status contains, which is the closest analog to parameter semantics for a no-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 states a specific verb ('Return') and resource ('current service and project status'), and enumerates the exact contents: initialization phase, index statistics, staging area, and warnings. It also distinguishes itself from sibling tools by explicitly saying it is not a code understanding tool, which helps an agent tell it apart from pce_query and pce_impact.

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 explicit when-to-use guidance: confirm PCE availability, check whether the index has been built, or diagnose init/query/impact/sync issues. It also gives a best practice ('When unsure whether to call init or sync first, check status') and an explicit avoid statement ('Do not use this as a code understanding tool'), which routes the agent away from inappropriate alternatives.

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 full behavioral disclosure burden. It adds some useful context: 'refreshes index state' and explicitly states it 'does not explain code.' However, it does not disclose side effects, persistence, reversibility, or idempotence. For a mutation-like operation, this is a moderate but not severe gap.

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 well-structured with labeled sections (Purpose, When to use, Best practice, Avoid). It is front-loaded with the core purpose and every sentence provides actionable guidance without redundancy or fluff.

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 zero-parameter tool, the description covers purpose, usage timing, best practice, and exclusions. It does not mention return values or behavior when the index is already in sync, but these are minor given the tool's simplicity and the presence of sibling tools like pce_status for checking state.

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 zero parameters and 100% schema description coverage, so the baseline is 4. No parameter documentation is needed since there is nothing to 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 specific verb and resource: "Synchronize Serena and PCE index state" and explains its goal: "so subsequent query / impact calls work against the latest code." It clearly distinguishes itself from query/impact tools by positioning itself as a preparatory synchronization step, and the 'Avoid' section separates it from code understanding 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 provides explicit 'When to use' guidance (after batch code modifications, deletions, renames, structural changes), 'Best practice' (one batch call per round of changes), and 'Avoid' (not for code understanding). This gives an agent clear decision criteria for when and how to invoke the tool.

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