Return the distribution of skill tags across all active operator claims on the TensorFeed Agent Directory. Sorted by count desc. Useful for agents discovering what kinds of work other operators advertise on TF, OR for agent-marketplaces wanting to mirror TF's controlled vocab.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions that the tool acts on 'all active operator claims' and is sorted by count descending, implying a read-only operation. However, it does not explicitly disclose whether it is destructive, requires authentication, or other behavioral traits. The description adds some context but not comprehensive transparency.
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?
The description is extremely concise: two sentences, front-loaded with the core action, and a second sentence providing use cases. Every sentence adds value, no fluff.
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?
Given zero parameters and no output schema, the description adequately conveys what the tool returns and its purpose. It specifies 'distribution of skill tags', 'sorted by count desc', and 'across all active operator claims'. However, it does not describe the output format (e.g., JSON structure), which would be helpful for an agent. It is mostly complete for a simple tool.
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?
The tool has zero parameters and the input schema is empty, so schema coverage is 100% by default. The description does not need to explain parameter semantics. Per guidelines, a baseline of 4 is appropriate.
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 clearly states the tool returns a distribution of skill tags across all active operator claims, sorted by count descending. It provides explicit use cases for agents discovering work types and for agent-marketplaces mirroring controlled vocabulary. This distinguishes it from sibling tools like 'search_agent_directory'.
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 explains when to use the tool (for discovering work types or mirroring controlled vocab), but does not explicitly state when not to use it or provide alternatives. The context is clear, but exclusions are missing.
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.
Several security/vulnerability tools (check_ai_supply_chain_risk, get_cve_record, get_osv_advisory_by_id, get_osv_advisory_for_package) have overlapping purposes, making it potentially confusing to choose the right one. Other tools are more distinct, but the ambiguity in this cluster lowers the score.
All names are snake_case and mostly follow a verb_noun pattern, but the verbs are inconsistent (check, get, list, lookup, query, register, route, search, submit, whats_new). Some tools use 'get' while others use 'check' for similar retrieval actions, and 'whats_new' does not fit the verb_noun pattern.
With 27 tools, the count exceeds the 25 threshold for 'too many', even though the broad scope spans many domains. The sheer number makes the server feel heavy and harder to navigate.
The server covers a wide range of data domains, but notable gaps exist: no management of watch subscriptions (only register), no CVE search, and no model search. Write operations are minimal, leaving some workflows incomplete.
