Skip to main content
Glama

search_asset_faces

Identify people in an asset by matching detected faces against the organization-wide face index, then reindex the asset with the matched person tags.

Instructions

Run face search on an asset using the org-wide face index; matches detected faces to indexed person tags. Reindexes the asset on success.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesID of the resource
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 discloses a non-obvious side effect ('Reindexes the asset on success'), which is valuable, but it does not mention other behavioral traits such as required permissions, potential data modifications, or failure behavior. The side-effect disclosure adds context but is not comprehensive.

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 two concise sentences, front-loaded with the core purpose and then a side effect. Every word adds value, with no redundancy or filler. It is well-structured 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 relatively simple signature (one param, no output schema) and the disclosure of a side effect, the description covers the main operation. However, it does not explain what the search returns (e.g., matched faces, status) or any failure conditions, which would be important for a search tool with no output schema. Some gaps remain in understanding the full behavior.

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?

The input schema has 100% coverage with a description for the single parameter ('ID of the resource'), so the baseline is 3. The description adds some clarity that the resource is an asset, but it does not specify how the 'id' should be formatted or used beyond schema information, and no additional parameter details are provided.

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 states the tool's function with a specific verb ('Run face search') and resource ('on an asset'), and mentions the use of the org-wide face index and matching to person tags. It distinguishes from sibling tools like tag_asset_face or detect_video_faces by focusing on searching/matching rather than tagging or detecting, though it does not explicitly name any sibling.

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 is provided on when to use this tool versus alternatives, such as when to use search_asset_faces instead of detect_video_faces or tag_asset_face. The description implies usage based on its function, but lacks any explicit context, exclusions, or alternative tool references, making it hard for an agent to decide among many face-related siblings.

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/mediagraph-io/mediagraph-mcp'

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