Search AI products, models, agents and organizations by keyword. Returns ranked matches with scores and why they matched.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Search query | |
| tag | No | ||
| type | No | ||
| limit | No | ||
| continent | No | L1 continent name, e.g. 'AI Coding' | |
| score_mode | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the behavioral burden. It does add useful disclosure: results are ranked and include scores and match reasons. However, it doesn't cover pagination, scoring semantics, or any side effects, which is a moderate gap 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two dense sentences with no filler—action, resource, and result are front-loaded. It earns each word without restating schema fields.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 6-parameter search tool with no annotations and no output schema, the description is underspecified. It explains the core query and return shape but omits parameter semantics and edge cases, making full utilization difficult.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only q and continent have schema descriptions; tag, type, limit, and score_mode are largely undocumented, and the tool description does not compensate. The phrase 'ranked matches with scores' hints at score_mode but doesn't explain its enum values or the limit/tag/type filter behaviors. Coverage is only 33%, so agents would have to guess at most parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a specific action (search by keyword), target resources (AI products, models, agents, organizations), and return value (ranked matches with scores and why they matched). This separates it from siblings like get_entity and list_entities, though 'agents' doesn't map to the type enum's values.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for keyword-driven discovery, but it doesn't state when to prefer search_entities over list_entities or get_entity, nor mention any exclusions. There is no explicit when/when-not guidance, leaving the agent to infer use cases from the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a distinct role: get single entity, list entities, search entities, compare entities, get relations, get taxonomy, get tracks, get snapshot meta. Although list_entities and search_entities are related, their descriptions clearly separate filtering/ranking from keyword search.
All tool names follow a consistent verb_noun snake_case pattern using clear verbs: get, list, search, compare. There are no mixed conventions or vague generic names.
8 tools is well-scoped for a read-only knowledge/atlas server. Each tool covers a meaningful operation without redundancy or unnecessary bloat.
The set covers the full read-only lifecycle: discovery (list, search), detail (get_entity), analysis (compare, relations), and context (taxonomy, tracks, snapshot meta). No obvious gaps exist for the apparent purpose of exploring and comparing scored entities.