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houtini-ai

Google Knowledge Graph MCP

by houtini-ai
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TypeScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.7

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one searches by name/topic, the other retrieves by specific MID. There is no overlap in functionality, and an agent can easily choose the right tool based on whether it has an ID or a query.

    Naming Consistency4/5

    Both tools follow a verb + 'knowledge_graph' pattern, with 'search_knowledge_graph' and 'lookup_knowledge_graph_entities'. The second includes an extra noun ('entities'), but the shared prefix and consistent style make them predictable and readable.

    Tool Count3/5

    With only two tools, the server feels minimal but functional for its stated purpose of querying the Knowledge Graph. It falls into the borderline range where the count is thin but not unreasonable for a focused utility.

    Completeness4/5

    The two core operations for a knowledge graph API—search and lookup by ID—are covered. Missing features like batch lookup or relation traversal are minor gaps that can be worked around with repeated calls.

  • Average 3.6/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 4 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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?

    No annotations are provided, so the description carries full burden. It states it 'Returns structured information' and refers to Google's public knowledge base, which adds some context. However, it doesn't disclose rate limits, authentication requirements, or explicitly state it's a read-only operation.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single sentence that efficiently states the tool's purpose and return type. No redundant information.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The tool has no output schema and no annotations. The description provides a high-level overview but doesn't specify result structure, pagination, or how it differs from the sibling lookup tool. For a straightforward search tool, this is functional but lacks depth.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 100%, so the description is not expected to expand on parameters. It mentions 'by name or topic' which maps to the query parameter, but no additional semantic value is added beyond the schema.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly identifies the tool as a search operation against Google Knowledge Graph, with specific resource (entities) and examples of what it returns. While it distinguishes from the sibling by using 'search' vs 'lookup', it doesn't explicitly explain the differentiation.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No guidance on when to use search vs lookup_knowledge_graph_entities. The description doesn't mention alternatives or exclusion criteria, leaving the agent without decision-making context.

    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 for behavioral disclosure. It explains the MID format and that the tool looks up entities, which implies a read-only operation, but it does not mention what is returned, language handling, or any limitations. 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.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Two short sentences: the first states exact purpose, the second provides usage context and MID examples. Every word earns its place; no fluff.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    With only two parameters and no output schema, the description is reasonably complete for usage. However, it does not explain what the lookup returns (e.g., entity details, names, descriptions) or mention the languages parameter, leaving some ambiguity for the agent.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 100% with descriptions for both `ids` and `languages`. The description adds a second MID format example (/g/11b6vwtjpg) beyond the schema's single example, providing a slight enhancement, but it does not discuss the `languages` parameter. Baseline 3 is appropriate.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states 'Look up specific Knowledge Graph entities by their Machine IDs (MIDs)', which is a specific verb+resource+scope. It also distinguishes from the sibling tool by explicitly mentioning use after a previous search.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description says 'Use this when you already know the entity IDs from a previous search', which gives clear context for when to use it. It implies search_knowledge_graph is for when you don't know IDs, but doesn't explicitly name the alternative or state when not to use it.

    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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