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Glama

Agent Ready

by io.github.mlava

Ask Agent Ready in natural language

ask
Read-onlyIdempotent

Natural-language search (NLWeb /ask) over Agent Ready's own content — scoring methodology, the check registry, the specs it validates, and the content library (explainers, comparisons, how-to guides, glossary). Returns Schema.org-typed result objects. Optional itemType narrows to a corpus type; mode 'summarize' adds an extractive summary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesNatural-language question to search Agent Ready's docs, scoring methodology, check registry, and content library (explainers, comparisons, how-to guides, glossary).
modeNo"list" (default) returns matching items; "summarize" returns a synthesized answer.
itemTypeNoRestrict results to a content type: methodology, checks, specs, llms-txt, check, page (explainers/guides/glossary), or any (default: all types).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageNoExtractive summary when mode is 'summarize'.
resultsYesMatching result items (Schema.org-typed).

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, so the description need not repeat that. The description adds useful behavioral context: results are Schema.org-typed, mode 'summarize' adds an extractive summary, and itemType narrows the corpus. No contradictions with annotations.

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 concise, consisting of three sentences that front-load the core purpose, then add return type and optional parameter behavior. Every sentence earns its place, with no 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?

With an output schema present, the description does not need to detail return values. It fully covers what content is searched, the optional narrowing and summarization modes, and the result type. It is complete for a search tool, though it omits any mention of pagination or result limits, which is slightly expected for a search operation.

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% with each parameter well-described, so the baseline is 3. The description adds slight extra meaning by explaining itemType narrows to a corpus type and mode 'summarize' adds a summary, but these largely echo the schema and no new syntax or examples are provided.

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 performs natural-language search over Agent Ready's own content, listing specific corpora (methodology, check registry, specs, content library). It also mentions the return type (Schema.org-typed objects), making the purpose specific and distinct from sibling scan tools.

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

Usage Guidelines4/5

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

The description provides clear context on what content is searched and that mode/itemType can adjust the query, implying when to use it. However, it does not explicitly mention when not to use it or provide alternatives like the sibling tools get_scan and scan_site.

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

All Agent Ready toolsInspect
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TDQS

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: ask searches content, scan_site initiates scans, and get_scan retrieves scan results. There is no overlap or ambiguity between them.

Naming Consistency4/5

Tool names mostly follow a verb-based pattern (ask, scan_site, get_scan), with slight variation in that 'ask' lacks an explicit object while the others are verb_noun. Overall consistent enough to be predictable.

Tool Count5/5

Three tools is well-scoped for a focused scanner/content-search server. Each tool serves a necessary function without bloat or redundancy.

Completeness4/5

The scan lifecycle (initiate and fetch) is covered, and content search is provided. Minor gaps like listing past scans or canceling scans exist, but these are workaroundable.

Resources