Skip to main content
Glama

chromadb_search

Search ChromaDB collections via semantic vector similarity, automatically embedding text queries to find related results.

Instructions

Search ChromaDB collections with semantic/vector similarity search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
envNoEnvironment: prod (default), lab
queryYesSearch query (text, not vector — will be embedded automatically)
n_resultsNoNumber of results to return
collectionYesCollection name to search in
Behavior2/5

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

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It fails to mention that this is a read-only operation, that the query is automatically embedded, or any other behavioral details such as performance implications or access requirements. The schema's parameter descriptions cover the embedding note, but the main description adds no transparency.

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 a single sentence that efficiently communicates the tool's core function. It is appropriately sized for a simple search operation, though it could be slightly more informative without becoming verbose.

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

Completeness2/5

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

The description lacks information about return values or result format. Since there is no output schema, the description should at least hint at what the agent can expect (e.g., document IDs, scores, metadata). This gap makes the tool's behavior less predictable.

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 baseline is 3. The main description does not add any additional meaning beyond the schema; it only repeats the tool's purpose. The parameter details are already well-explained in the schema, so no further compensation is needed.

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 searches ChromaDB collections using semantic/vector similarity, which is a specific verb+resource pairing. It distinguishes itself from sibling tools like chromadb_collections (managing collections) and chromadb_upsert (inserting/updating data) by specifying the search functionality.

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?

The description does not provide any guidance on when to use this tool versus alternatives (e.g., when to search vs. list collections or upsert data). No when-not or exclusion criteria are mentioned, leaving the agent with insufficient context for tool selection.

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/ynotopec/infocepo-infra-mcp'

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