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MCP Verify

by io.sentinelsignal

recommend_servers

Read-only

Recommend MCP servers for a plain-language task and return ranked matches with install config.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesPlain-language task description such as 'I need an MCP for healthcare denial scoring in OpenAI connectors'.
limitNoMaximum number of recommendations to return.
capabilitiesNoOptional explicit capability constraints that override or extend the inferred task capabilities.
client_targetNoOptional target client or integration surface.
risk_toleranceNoHow much tool-surface risk is acceptable.
auth_preferenceNoPreferred auth mode.

Schema Changelog

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

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

With readOnlyHint already declared, the description adds value by specifying the output behavior: returning ranked matches with install config. This goes beyond the annotation, though it does not disclose ranking methodology or limitations. Still, the added context is useful for a read-only recommendation tool.

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 a single concise sentence that front-loads the action verb 'Recommend' and clearly conveys the purpose and output. There is no wasted wording or unnecessary 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?

Despite having no output schema, the description provides a clear picture of the tool's main behavior and output (ranked matches with install config). With 100% schema coverage for inputs, the description is sufficiently complete for a recommendation tool, though it could elaborate on what 'ranked matches' and 'install config' entail.

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 coverage is 100%, so the parameter descriptions in the schema already provide full meaning. The tool description itself adds minimal parameter context beyond noting the 'plain-language task' input, which aligns with the 'task' parameter. Since the schema handles all parameters, a baseline score of 3 is appropriate.

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?

The description clearly states the tool's function: to recommend MCP servers for a plain-language task and return ranked matches with install config. This distinguishes it from siblings like search_servers (which searches) and compare_servers (which compares), by emphasizing a natural-language input and recommendation output.

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 implies usage for plain-language task descriptions but does not explicitly state when to use this tool over alternatives such as search_servers or compare_servers. It lacks explicit exclusions or alternative guidance, though the focus on 'plain-language' hints at the intended input style.

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

B3.4/5.0
Disambiguation4/5

Most tools target distinct resources and actions (e.g., get_agent vs. get_drift_report). A few pairs like search/search_servers and fetch/get_server_report overlap in intent, but descriptions clarify differences in output and purpose. Agents may need to read carefully but can generally tell tools apart.

Naming Consistency4/5

Nearly all tool names follow a verb_noun snake_case pattern (list_agents, create_mandate, export_policy). The bare aliases 'fetch' and 'search' deviate slightly, but are clearly intentional, and there is no mix of camelCase or other styles. Overall consistent and predictable.

Tool Count3/5

22 tools is on the heavy side, falling into the '16-25' borderline range. While each tool appears to serve a distinct function, the set could be consolidated (e.g., the multiple 'get_*_options' tools). It is not excessive enough to be chaotic, but it is above the ideal 3-15 range.

Completeness4/5

The tool surface covers core workflows for server search, comparison, report generation, policy export, mandate creation, agent risk management, and decision-making. Minor gaps exist, such as no create/update/delete for most resources and no subscription creation, but the domain is read/decision-heavy and core lifecycle coverage is adequate.

Resources