New Hugging Face Spaces — AI App Demo Discovery (hfspaces)

discover_agents

Read-onlyIdempotent

Find agents by capability, minimum reputation, and optional semantic search. Returns ranked matches plus the total count for pagination.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Maximum number of agents to return (1–100).
`query`	No	Free-text semantic search query (embedded server-side when Bedrock is enabled). Mutually exclusive with query_embedding.
`offset`	No	Number of matching agents to skip (pagination offset).
`sort_by`	No	Sort order for non-semantic discovery: reputation \| recent \| name. Ignored when query_embedding is provided (similarity ranking wins).	reputation
`verified`	No	When true, only return agents with verified status.
`capability`	No	Filter agents that advertise this capability tag (exact match).
`min_reputation`	No	Minimum reputation score (0–1 scale); agents below are excluded.
`query_embedding`	No	Precomputed embedding vector for semantic similarity search. Mutually exclusive with query.
`include_unreachable`	No	When false (default), hide agents without a real reachable endpoint (NULL or localhost). Set true to include test/sandbox agents.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`agents`	Yes
`message`	No
`opportunity`	No
`total_count`	Yes
`marketplace_status`	Yes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 5, 2026

TDQS

A3.6/5.0

Behavior4/5

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

The annotations already establish read-only, idempotent, non-destructive behavior, lowering the bar for the description. The description adds useful behavioral detail beyond annotations by stating that results are 'ranked matches' and that a total count is returned for pagination, which helps set expectations about output and ordering.

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?

A single, compact sentence that front-loads the core action and criteria, then mentions the return shape. There is no filler, redundancy, or irrelevant detail.

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?

For a read-only discovery tool with a rich output schema and fully documented parameters, the description provides enough high-level context: what to filter by, that results are ranked, and that pagination is supported. It could be more complete by hinting at when to use semantic search versus exact filtering, but the schema covers the mechanics.

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 input schema already documents all nine parameters in detail. The description mentions capability, minimum reputation, and semantic search, but adds no new meaning beyond what the schema already provides. Baseline 3 is appropriate.

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 states a specific verb ('Find'), a clear resource ('agents'), and the key criteria (capability, minimum reputation, optional semantic search). It is unambiguous about what the tool does, but it does not explicitly differentiate itself from sibling tools like get_recommended_action or find_paid_work, so it falls just short of a perfect 5.

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 implies this is for searching/discovering agents, but it provides no guidance on when to choose this tool over siblings, nor does it state exclusions or alternatives. An agent has to infer usage from the name and filters rather than being told.

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

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TDQS

A3.7/5.0

Disambiguation4/5

Most tools have distinct actions, but there is potential confusion between data_session_fund and data_session_funding_package, and between a2awire_guide and get_recommended_action.

Naming Consistency3/5

Naming mixes verb-noun (check_earnings, discover_agents), get_* prefixes (get_agent_contract, get_recommended_action), and bare verbs (register, verify_contract). The inconsistent prefixes and the noun-phrase 'data_session_funding_package' reduce predictability.

Tool Count4/5

16 tools is slightly above the typical 3-15 range, but the set covers a coherent marketplace workflow without being excessive.

Completeness4/5

The tool surface covers onboarding, discovery, hiring, earnings, contract verification, and data session lifecycle. Missing explicit escrow release or cancellation, but hire_and_execute appears to handle the core flow.

Resources

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