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Vertex AI MCP Server

Vertex AI MCP Server

by shariqriazz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

get_doc_snippets

Retrieve precise code snippets or concise answers from official documentation for technical queries. Specify the software/library topic and your query to get exact solutions without unnecessary explanations.

Instructions

Provides precise, authoritative code snippets or concise answers for technical queries by searching official documentation. Focuses on delivering exact solutions without unnecessary explanation. Uses the configured Vertex AI model (gemini-2.5-pro-exp-03-25) with Google Search. Requires 'topic' and 'query'.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "topic": "<topic>",
      "query": "<query>"
    },
    "name": "get_doc_snippets"
  }
}

Input Schema

NameRequiredDescriptionDefault
include_examplesNoOptional. Whether to include additional usage examples beyond the primary snippet. Defaults to true.
queryYesThe specific question or use case to find a snippet or concise answer for.
topicYesThe software/library/framework topic (e.g., 'React Router', 'Python requests', 'PostgreSQL 14').
versionNoOptional. Specific version of the software to target (e.g., '6.4', '2.28.2'). If provided, only documentation for this version will be used.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the tool's focus ('precise, authoritative', 'without unnecessary explanation'), method ('searching official documentation', 'uses Vertex AI model with Google Search'), and requirement ('Requires topic and query'). However, it lacks details on rate limits, error handling, or response format, which are important for a search tool.

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 appropriately sized and front-loaded, with three sentences that each earn their place: the first states the purpose, the second details the method, and the third specifies requirements. There is no wasted text, making it efficient and easy to parse.

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?

Given the tool's complexity (search with AI model), no annotations, and no output schema, the description is somewhat complete but has gaps. It covers purpose and method well, but lacks details on behavioral traits like rate limits or response structure, which could hinder an agent's ability to use it effectively in all contexts.

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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description mentions 'topic' and 'query' as required, adding minimal value beyond the schema. It does not explain parameter interactions or provide additional context, 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.

Purpose5/5

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

The description clearly states the tool's purpose with specific verbs ('provides', 'searching') and resources ('code snippets', 'concise answers', 'official documentation'). It distinguishes from siblings like 'answer_query_direct' or 'explain_topic_with_docs' by emphasizing precision, authority, and lack of unnecessary explanation.

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 provides clear context for when to use this tool ('for technical queries', 'searching official documentation'), but does not explicitly state when not to use it or name alternatives among siblings. It implies usage for exact solutions without explanation, which helps differentiate from more verbose tools.

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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