developer-tools-mcp-server

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

Annotations already declare readOnly and openWorld hints. The description adds value by enumerating the specific returned metrics (star count, fork count, etc.) and indicates read-only behavior consistent with annotations. Does not disclose rate limits or error handling, but the bar is lower given 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, front-loaded with the primary action and resource, then a concise list of return fields and use cases. No redundant or filler content.

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 simple one-parameter schema and no output schema, the description adequately explains what the tool returns and when to use it. It could mention failure behavior (e.g., repo not found) but is otherwise complete for this complexity level.

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 single parameter 'repo' is fully documented in the schema with format and examples, providing 100% coverage. The description adds no additional parameter-level meaning beyond what the schema already offers, so 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?

Clearly states it fetches detailed statistics and metadata for a specific GitHub repository, listing key return fields. This distinguishes it from sibling search tools like search_github and other package-specific 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?

Provides context on when to use (evaluating open-source projects, competitive analysis, monitoring project health) but does not explicitly contrast with alternatives like search_github. Clear usage context, though no exclusions are stated.

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, but the description adds valuable behavioral context by enumerating exactly what data is returned (latest version, download stats, dependencies, license, GitHub link) and that it fetches from the NPM registry. It does not mention error handling or rate limits, but the read-only nature and return payload are well disclosed.

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: the first states the core action and return contents, the second gives usage scenarios. Every word earns its place with no redundancy, and the most important information 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?

For a simple read-only lookup tool with one parameter and no output schema, the description covers the essential behavior and return fields. It is sufficient for an agent to select and invoke the tool, though it could mention handling of nonexistent packages or network failures.

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 for the single parameter, fully explaining that package_name must be the exact published name with examples. The description does not add further parameter-specific meaning, so 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 clearly states the tool looks up Node.js package information from the NPM registry, specifying the exact resource and action. It distinguishes itself from siblings like get_pypi_package and get_github_repo by focusing on npm packages and lists specific return data (version, downloads, dependencies, license, etc.).

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 use cases: evaluating JavaScript libraries, checking maintenance status, and reviewing package popularity. It does not name alternatives or state when not to use it, but the context is clear relative to sibling tools. This is a strong 'when to use' statement, lacking only explicit 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?

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is known. The description adds value by specifying the exact data returned (current version, download counts, dependencies, release history, homepage, PyPI URL), which is useful behavioral context beyond the annotations. 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 two concise sentences, front-loaded with the action and resource. Every sentence adds value—first the purpose, then the return values and use cases. 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 single-parameter read-only tool with strong annotations and clear return field enumeration, the description is complete. It covers what the tool does, what it returns, and when to use it. The absence of an output schema is mitigated by the explicit list of returned data.

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 with a clear description of 'package_name' (e.g., 'numpy', 'django'). The description does not add much beyond the schema, but the schema itself is sufficiently descriptive, 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 clearly states the tool's function with a specific verb 'Retrieve' and a specific resource 'Python package information from PyPI'. It lists concrete return fields (version, download counts, dependencies, etc.), which distinguishes it from sibling tools like get_npm_package and get_github_repo.

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 states use cases: 'Use for Python library evaluation, dependency analysis, or checking package quality metrics.' This gives clear context for when to use the tool, but it does not mention when not to use it or name alternative tools for other package registries.

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 and openWorldHint=true, so safety is known. The description adds behavioral detail by specifying the return fields (titles, authors, abstracts, dates, PDF links), which helps the agent anticipate the output. It does not mention any limitations like metadata-only search, but that is not critical.

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 crisp sentences without fluff. The first sentence covers purpose and output, the second covers usage. All sentences earn their keep.

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 two simple parameters, full schema descriptions, and read-only annotations, the description provides sufficient context. It lists output fields and usage, making it complete for a search tool without needing an output schema.

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 both parameters with descriptions, so coverage is 100%. The description reinforces the query domain but adds no new parameter semantics beyond the schema. Baseline 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 opens with 'Search arXiv for academic papers in computer science, machine learning, AI, physics, and mathematics,' which clearly states the action, resource, and domain scope. It differentiates from sibling search tools like search_google_scholar by naming arXiv specifically and listing return fields.

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 phrase 'Use for researching algorithms, ML techniques, or emerging CS topics' provides clear usage context. It does not explicitly name alternatives like search_google_scholar or exclude non-academic queries, so it stops short of a full 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 declare readOnlyHint and openWorldHint, so safety is covered. The description adds useful context about return values (star count, description, language, URL) and the scope of results, which goes beyond the 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, front-loaded with the primary action, and every clause earns its place. No fluff, no repetition, and appropriately sized for the tool's simplicity.

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 search tool with 2 well-documented parameters, open-world hints, and no output schema, the description covers purpose, use cases, and return shape. Nothing essential 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?

Schema coverage is 100%, so the baseline is 3. The description does not add any extra semantic detail beyond what the schema already provides; it merely restates that it searches by keyword, which the 'query' parameter already says.

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 a specific verb and resource ('Search GitHub repositories by keyword') and explains what it returns. Differentiates itself from siblings like get_github_repo (search vs specific) and other search tools (Arxiv, Stack Overflow) through its scope.

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 to use it for 'finding libraries, examples, or competitive projects in specific domains,' giving a clear intended use case. Does not explicitly mention when not to use it, but the purpose is distinct enough from siblings.

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 and openWorldHint=true, covering the safety profile. The description adds that it 'Returns paper title, authors, publication details, citation count, and link to paper,' which is valuable behavioral/return information beyond the annotations. No contradiction.

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, front-loaded with the main action, then return fields and usage examples. Every sentence serves a purpose with no redundancy, making it highly concise and well-structured.

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 two-parameter, read-only search tool with no output schema, the description provides everything needed: purpose, return fields, and use cases. It is self-sufficient for an agent to decide whether and how to invoke it.

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 description covers 100% of the parameters ('query' and 'max_results' with descriptions and a default), so the baseline is 3. The tool description does not add additional parameter detail beyond what the schema already provides.

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 opens with 'Search Google Scholar for computer science research papers, citations, and academic publications,' specifying both verb and resource. It clearly distinguishes the tool from siblings like search_arxiv by naming Google Scholar and its CS focus, and it lists the return fields, reinforcing what the tool does.

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 states 'Use for finding research on CS topics, reviewing state-of-the-art, or citation tracking,' giving clear use cases. However, it does not explicitly mention alternatives or when not to use it, so it falls short of the highest bar for usage guidance.

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 and openWorldHint=true, so the safety profile is clear. The description adds value by detailing the return content (questions, answer count, view count, accepted answer snippet, tags, link), which goes beyond annotations. No contradiction 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 two sentences, front-loaded with the tool's purpose, then return details, then usage context. Every sentence earns its place with no redundancy.

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 search tool with 2 parameters, no output schema, and supportive annotations, the description is complete enough. It covers purpose, return content, and usage. It could mention potential limitations (e.g., API rate limits) but that is not critical for this 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 description coverage is 100%, so both parameters (query and max_results) are well-documented in the schema itself. The description does not add extra parameter-specific semantics beyond what the schema provides, supporting the baseline score of 3.

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 clearly states it searches Stack Overflow for programming questions, solutions, and code examples, and lists specific return fields (answer count, view count, accepted answer snippet, tags, link). This specific verb+resource distinguishes it from sibling tools like search_github or search_arxiv.

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 usage context: 'Use for troubleshooting, code examples, or finding solutions to common problems.' This clarifies when to use the tool but does not explicitly mention alternatives or when not to use it, which would warrant a 5.

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