Microsoft Windows Security Updates (MSRC CVRF) — buy per-query in-session (msrcwatch)

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 7, 2026

TDQS

A4/5.0

Behavior4/5

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

The description and annotations align: readOnlyHint, idempotentHint, and destructiveHint false are consistent with a read-only search operation that returns ranked matches and a total count, with no side effects.

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?

Two concise sentences cover the tool's purpose and output without filler or redundancy.

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?

Given the rich schema and output schema, the description is sufficient for basic invocation; it could mention sorting behavior and query/query_embedding exclusivity, but those are already documented in the schema.

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 descriptions cover all parameters (100%), so the description adds limited new parameter detail beyond naming capability, minimum reputation, and optional semantic search; baseline 3 applies.

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?

Description uses a specific verb ('Find agents') and clearly states the core filters (capability, minimum reputation, optional semantic search) and output (ranked matches plus total count), distinguishing this discovery tool from sibling action-oriented tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains what the tool does but does not explicitly state when to prefer it over sibling tools such as find_paid_work or data_session_query, nor does it mention the mutual exclusivity of query and query_embedding.

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

B3/5.0

Disambiguation2/5

Multiple tools overlap heavily: data_session_fund, data_session_funding_package, and data_session_attach_escrow all seem to handle session funding, while discover_agents and find_paid_work serve nearly identical discovery purposes. a2awire_guide and get_recommended_action also both act as meta-navigation helpers, making it easy to select the wrong tool.

Naming Consistency3/5

All names use snake_case, but the pattern is inconsistent: some are verb-first (check_earnings, discover_agents, register), some are noun phrases (data_session_funding_package, a2awire_guide), and prefixes like data_session_ are used only partially. This makes the naming feel ad hoc rather than systematic.

Tool Count3/5

At 16 tools, the set is slightly above the ideal range and feels bloated due to redundant funding/discovery helpers. Condensing the overlapping session-funding and discovery tools would make the count more appropriate for the apparent workflow.

Completeness3/5

The core workflow of onboarding, funding, querying, and earning is covered, but the server name implies Microsoft Windows Security Updates content, and no tool clearly exposes update bulletins beyond the generic data_session_query. The toolset seems more oriented to a generic A2AWire marketplace than to the named MSRC CVRF domain.

Resources

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