mcp-server-searchapi
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool targets a distinct content source (YouTube, web, news, scholar) with specific result types, leaving no ambiguity about which tool to use for a given search need.
Naming Consistency5/5All tool names follow a uniform '<source>_search' pattern, making the tool set highly predictable and easy to navigate.
Tool Count5/5Four tools is a well-scoped set for a search API server, covering the most common search verticals without unnecessary bloat.
Completeness4/5The surface covers web, news, academic, and video search, which are fundamental. Minor gaps like image or social search exist but are not critical for the apparent purpose.
Average 4/5 across 4 of 4 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 19 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the behavioral transparency burden. It discloses the return format (videos with specific fields), which is useful, but it does not mention result limits, pagination, ordering, or any potential side effects. Lacking these details, transparency is adequate but incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that conveys the core action and return value without redundancy. Every word contributes meaning, and it is 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.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool with three fully documented parameters and no output schema, the description is largely complete. It clearly states what the tool returns, though it omits details like result count or pagination behavior. This is a minor gap for a straightforward search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are already fully documented in the schema. The description adds no additional meaning beyond what the schema provides for 'query', 'gl', and 'hl', earning the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: 'Search YouTube via SearchApi' and lists the returned video fields (title, link, channel, views, published time, length). This clearly distinguishes it from sibling tools like web_search or scholar_search, which target different content domains.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a user wants YouTube-specific search results, but it does not explicitly state when to use this tool versus alternatives or mention exclusions. There are no alternative tool names or conditions given, so guidance is only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the return types (title, link, source, date, snippet) which is helpful behavioral information. However, it does not explicitly state that it is a read-only operation, any rate limits, or other behavioral constraints, leaving some ambiguity for an agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the main purpose and lists the return fields. Every word earns its place and there is no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that this is a simple search tool with no output schema, the description adequately explains the purpose and return content. It could mention typical search tool details like result limits or sorting, but for a straightforward news search, the provided information is sufficient 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.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% since all four parameters have clear descriptions (e.g., gl as country code, hl as interface language, time_period with enum values). The description adds no additional parameter semantics beyond what the schema already provides, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches Google News via SearchApi, which is a specific verb and resource. It distinguishes itself from siblings like web_search, youtube_search, and scholar_search by explicitly focusing on news articles and listing the returned fields (title, link, source, date, snippet).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context that this tool is for searching news articles, implying it should be used when news-specific results are needed. However, it does not explicitly exclude other search tools or mention alternatives, so it lacks explicit 'when-not-to-use' guidance but is still contextually clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the return fields (title, link, publication info, citation count, snippet), which is useful. However, it does not mention any limitations, rate limits, authentication needs, or side effects. For a read-only search tool this is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that packs the essential information: what it does (search Google Scholar), the API, and what it returns. It is front-loaded with the verb and resource, with zero wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity, no output schema, and complete parameter schema, the description adequately covers the return values. It lists all key result fields. It could mention pagination or the year_from filter, but the schema covers those, so the description is sufficiently complete for this simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers all three parameters with clear descriptions (query, page, year_from), so baseline is 3. The description does not add any extra meaning beyond the schema; it only restates that it is a search query. The schema itself is sufficient.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches Google Scholar, a specific academic resource, and differentiates it from sibling tools like web_search and news_search by specifying it returns academic results with citation counts. The verb 'Search' plus resource 'Google Scholar' makes the purpose immediately clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for scholarly/academic queries via the phrase 'academic results' and 'scholarly search query' in the schema. This clearly distinguishes it from general web/news/youtube searches, though it does not explicitly state when not to use it or name alternatives. The context is clear enough to infer appropriate use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It clearly explains the expected output: organic results with title, link, snippet, and optional answer box, knowledge graph, and related searches. This provides meaningful insight into tool behavior beyond the tool's name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that directly states the action and the return format. Every word earns its place, with no redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool with no output schema, the description provides enough context about what results will be returned. It does not explain every parameter, but the schema covers those details, making the description reasonably complete for selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameter details are fully documented in the schema. The tool description does not add additional meaning about parameter usage, such as query formatting or how 'location' differs from 'gl', but this is acceptable given the schema already covers it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: 'Search the web via Google (SearchApi)'. It clearly differentiates from sibling tools by focusing on general web search rather than specialized sources like YouTube, news, or scholar.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for general web queries and contrasts implicitly with specialized sibling tools, but it does not explicitly state when to choose this tool over alternatives or provide exclusion criteria. Guidance is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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