Skip to main content
Glama

BuyAPI

by io.github.TheSnakeFang

vendors.resolve

Read-onlyIdempotent

First stop for category-specific vendor recommendations and vendor ID discovery. Finds BuyAPI vendor IDs for a user question; provide category when known.

Use this when the user asks which provider in a category fits their constraints. With a covered category, the response includes ranked results plus a top-3 decision matrix with fit labels, confidence, tradeoffs, cost notes, freshness, and sources. Do not use this for local coding/debugging/docs questions unless they involve choosing a software vendor or tool. If the category is outside BuyAPI's corpus, the tool returns an explicit "not in corpus yet" result instead of inventing vendors.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe user's question or task context for relevance ranking
categoryNoOptional category: database, auth, hosting, payments, email, analytics, feature-flags

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNo
queryNo
claimsNo
messageNo
resultsNo
coverageNo
decisionMatrixNo
suggestedNextStepsNo
availableCategoriesNo

Schema Changelog

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

  1. Changed3 schema fields changed
    • addedOutput schema / properties / claims
      Added value: +{
+  "items": {},
+  "type": "array"
+}
    • addedOutput schema / properties / coverage
      Added value: +{
+  "additionalProperties": {},
+  "propertyNames": {
+    "type": "string"
+  },
+  "type": "object"
+}
    • addedOutput schema / properties / decisionMatrix
      Added value: +{
+  "items": {},
+  "type": "array"
+}
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": {},
+  "properties": {
+    "availableCategories": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "kind": {
+      "type": "string"
+    },
+    "message": {
+      "type": "string"
+    },
+    "query": {
+      "type": "string"
+    },
+    "results": {
+      "items": {},
+      "type": "array"
+    },
+    "suggestedNextSteps": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    }
+  },
+  "type": "object"
+}
  3. Changed1 schema field changed
    • changedInput schema / properties / category / description
      Previous value: -"Optional category: database, auth, hosting, payments, email"New value: +"Optional category: database, auth, hosting, payments, email, analytics, feature-flags"
  4. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnlyHint and other annotations, the description adds meaningful behavioral context: it explains the response composition (ranked results, top-3 decision matrix with fit labels, confidence, tradeoffs, etc.) and the fallback behavior for out-of-corpus categories ('returns an explicit "not in corpus yet" result instead of inventing vendors'). This is transparent and reassuring, especially for a discovery/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 well-structured and front-loaded: it opens with purpose ('First stop...'), then usage, then response details, then exclusions, then edge-case behavior. Each sentence contributes distinct information with no fluff or repetition. It is slightly longer than the calibration high example, but every sentence 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 complexity and the presence of an output schema, the description is complete. It covers the tool's primary use case, expected output content, explicit exclusions, and the out-of-corpus fallback. This is sufficient context for an AI agent to decide when to invoke the tool and what to expect, even without viewing the output 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 description coverage is 100%, so baseline is 3. The description adds some value by advising 'provide category when known,' which highlights an important usage nuance not in the schema. However, it does not elaborate on the query parameter beyond what the schema already says, and the category parameter is fully documented in the schema. Thus, the description adds minimal additional semantic value.

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 purpose with specific verbs: 'Finds BuyAPI vendor IDs' and 'category-specific vendor recommendations.' It explicitly distinguishes itself from siblings by being the 'First stop' and by describing its unique output (ranked results plus decision matrix), which differentiates it from vendors.compare, vendors.details, and other sibling 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 gives explicit when-to-use guidance: 'Use this when the user asks which provider in a category fits their constraints.' It also provides a when-not-to-use exclusion: 'Do not use this for local coding/debugging/docs questions unless they involve choosing a software vendor or tool.' However, it does not explicitly name alternative sibling tools for those cases, so it misses the 'alternatives' component of a 5.

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

All BuyAPI toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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