Skip to main content
Glama

search

Query documents within your role permissions using vector search, returning relevant snippets with source citations and metadata for secure corporate knowledge access.

Instructions

RBAC-aware vector search across allowed domains/collections.
Args:
  query: natural language query
  top_k: number of results to return
  include_general: include 'general' alongside role-specific domains
Returns a list of hits with:
  score, collection, domain, source, title, doc_type, tags, chunk_id, snippet

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
top_kNo
include_generalNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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 mentions RBAC awareness and lists return fields, but lacks disclosure of side effects, rate limits, or authentication requirements.

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?

The description is structured with a clear first sentence and a parameter list, but it is somewhat verbose and repeats information that could be inferred from the schema, though it remains efficient.

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?

Given the tool's simplicity (3 parameters, output schema exists), the description adequately covers purpose, parameters, and return format. It misses prerequisites or limitations but is largely complete.

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

Parameters4/5

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

The schema has 0% description coverage, but the docstring adds meaningful parameter explanations: 'natural language query', 'number of results to return', and 'include general alongside role-specific domains', which go beyond plain property names.

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 it is a vector search tool with RBAC awareness, specifying it searches across allowed domains/collections. It distinguishes itself from sibling tools like list_files and read_file by focusing on search.

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

Usage Guidelines3/5

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

The description implies usage for search with role-based access but does not provide explicit when-to-use or when-not-to-use guidance, nor does it compare to alternatives like list_files or read_file.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Nithishkaranam2002/Finrag--mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server