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Glama

BuyAPI

by io.github.TheSnakeFang

vendors.evidence

Read-onlyIdempotent

Returns recent reviewed BuyAPI evidence rows for a vendor, category, stack, or comparison.

Use this when the user asks why BuyAPI believes something, what sources support a recommendation, or what recent human/source/opinion/history signals exist. This is a trust and provenance follow-up, not the first recommendation tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum rows to return
subjectIdYesSubject ID, e.g. /database/supabase or database
subjectTypeYesEvidence subject type

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
evidenceYes

Schema Changelog

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

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": {},
+  "properties": {
+    "evidence": {
+      "items": {},
+      "type": "array"
+    }
+  },
+  "required": [
+    "evidence"
+  ],
+  "type": "object"
+}
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is clear. The description adds behavioral context by specifying 'recent reviewed' evidence rows, indicating recency and a review filter. It does not mention pagination or result ordering, but the output schema covers return structure, so this is sufficient.

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 two sentences, front-loaded with the core function, and the second sentence provides usage context. No wasted words, all information earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (3 params, 2 required), comprehensive annotations, and an output schema, the description fully covers the tool's purpose, scope, and usage context. It even includes a direct use-case trigger and exclusions, making it complete for an agent to select correctly.

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?

The input schema already provides descriptions for all three parameters (limit, subjectId, subjectType), with 100% coverage. The description does not add further parameter-specific details, but it does implicitly map to subjectType via 'vendor, category, stack, or comparison.' Per the calibration baseline, a score of 3 is appropriate when schema does the heavy lifting.

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 'Returns recent reviewed BuyAPI evidence rows for a vendor, category, stack, or comparison.' It identifies a specific verb (returns), a resource (evidence rows), and the subject types (vendor, category, stack, comparison), distinguishing it from sibling tools like stacks.recommend or vendors.details.

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

Usage Guidelines5/5

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

The description explicitly says to use it when the user asks 'why BuyAPI believes something, what sources support a recommendation, or what recent human/source/opinion/history signals exist.' It also contrasts with the first recommendation tool, implying that recommendations should come from other tools and this one is for trust/provenance follow-up.

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

A4.4/5.0
Disambiguation5/5

Every tool targets a distinct purpose: stack examples vs. recommendation, vendor discovery vs. comparison vs. details vs. cost estimation vs. evidence. There is no functional overlap, and the descriptions clearly delineate boundaries.

Naming Consistency4/5

Tools follow a domain.action pattern (stacks.*, vendors.*), which is predictable. However, action names mix camelCase (findSimilar, estimateCost) with lowercase (recommend, compare), a minor inconsistency that doesn't hinder understanding.

Tool Count5/5

Seven tools cover the vendor and stack selection domain without being overwhelming or sparse. Each tool earns its place by addressing a distinct step in the workflow.

Completeness5/5

The tool set covers the complete workflow: initial vendor discovery (resolve), comparison (compare), detailed info (details), cost estimation (estimateCost), evidence retrieval (evidence), and stack examples/recommendations (findSimilar, recommend). No obvious gaps.

Resources