Skip to main content
Glama
nkapila6

mcp-local-rag

by nkapila6
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Google search

rag_search_google
Read-only

Searches Google and returns relevant pages as text, re-ranked locally by semantic similarity; falls back to ddgs metasearch with a warning when Google blocks or returns no results.

Instructions

Search Google and return the most relevant pages as text, re-ranked locally by semantic similarity. Google often blocks scrapers. When it returns nothing, this retries with ddgs "auto" metasearch and sends a warning saying so.

Input Schema

NameRequiredDescriptionDefault
queryYesThe query to search for.
top_kNoNumber of top-ranked pages to fetch and return.
num_resultsNoNumber of initial search results to rank.
include_urlsNoWhether to include each page's source URL.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.1.1
    • changedInput schema / properties / include_urls / description
      Previous value: -"Whether to include URLs in the results."New value: +"Whether to include each page's source URL."
    • changedInput schema / properties / num_results / description
      Previous value: -"Number of results to return."New value: +"Number of initial search results to rank."
    • changedInput schema / properties / top_k / description
      Previous value: -"Use top \"k\" results for content."New value: +"Number of top-ranked pages to fetch and return."
  2. Changed4 schema fields changedv1.0.4
    • addedInput schema / properties / include_urls / description
      Added value: +"Whether to include URLs in the results."
    • addedInput schema / properties / num_results / description
      Added value: +"Number of results to return."
    • addedInput schema / properties / query / description
      Added value: +"The query to search for."
    • addedInput schema / properties / top_k / description
      Added value: +"Use top \"k\" results for content."
  3. Addedv1.0.1

TDQS

A3.5/5.0
Behavior4/5

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

Annotations only declare readOnlyHint and openWorldHint, so the description adds real value: it discloses the local semantic re-ranking pass, that Google frequently blocks scrapers, that empty results trigger a ddgs 'auto' metasearch retry, and that a warning is emitted when that happens. This is exactly the kind of reliability context an agent needs. Return shape is left to the output schema, which is acceptable.

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

Conciseness4/5

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

Three tight sentences with no filler, leading immediately with what the tool does before the reliability caveat. The hard line breaks in the raw text are cosmetic and do not impede reading.

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

Completeness4/5

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

For a 4-parameter read-only search tool with a full output schema and annotation coverage, the description supplies the important missing context: local re-ranking, blocking risk, and the silent provider switch. Only the lack of sibling routing guidance keeps it from being fully complete.

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 both top_k and num_results are already documented in the schema; the description reinforces the two-stage model (rank N initial results, fetch and return the top K pages as text) but adds no syntax, format, or edge-case detail beyond that. Baseline 3 applies when the schema carries the parameter burden.

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?

States a specific verb and resource ('Search Google and return the most relevant pages as text') and adds the distinguishing trait of local semantic re-ranking. It does not, however, contrast itself against the obvious siblings (rag_search_ddgs, deep_research_google), so an agent must infer the boundary.

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 when-to-use or when-not-to-use guidance relative to alternatives. The only routing information is an internal fallback to ddgs on empty results, which describes runtime behavior rather than when the agent should choose this tool over rag_search_ddgs or deep_research_google.

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

Deploy Server

Other Tools