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agentery

by com.agentery
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Search providers

search_providers
Read-only

Use when someone wants to find or shortlist AI products, AI agents, MCP servers or APIs for a task, e.g. 'AI tool that transcribes sales calls' or 'MCP server to reply to Zendesk tickets'. Returns ranked candidates with what each does, a fit label (match / partial / weak, naming any mismatch), delivery type, price status (known / free / contact_sales / unknown; unknown is never free) and links. Requirements in the wording are checked per result; unverified ones are marked unknown. Results are candidates to check: open strong ones with get_provider_profile and compare finalists with compare_providers. Thin coverage returns no_match, never padding.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNomatch (default) or price_asc (cheapest observed price first; unpriced providers last)
limitNoMax results (1-50, default 20)
queryYesREQUIRED. Free-text description of the product job you are buying, e.g. 'customer support provider with Zendesk integration' or 'supplier invoice reconciliation'. Not for questions about Agentery itself (upvotes, endpoints): those return status unsupported_request.
billingNoOnly providers with one of these observed billing models, e.g. ["free","freemium","subscription","usage"]
filtersNoOptional: industry_fit[], integrations_available[], entity_type[] (agent|tool|infrastructure|service|marketplace|content-community), autonomy_level[] (assistant|workflow automation|agentic|infrastructure), minimum_evidence_quality (low|medium|high)
deploymentNoOptional (1.10.0). Keep only providers whose EVIDENCED deployment model matches: hosted = a vendor-published MCP endpoint exists (connect, nothing to install); self_hosted = a published install line (npx/uvx/docker/pip/git clone) and no vendor endpoint; both = both are documented; unknown = no evidence held. 'both' listings satisfy hosted and self_hosted. Evidence comes from the vendor's own pages and source directories (Glama/MCP registry attributes) — never guessed from a repository's existence.
request_refNoOptional task reference (rq_ + 12 hex) from an earlier answer. Continues that task: its remembered facts (quantities, budget, requirements, benchmark selection) apply to this call, and the answer keeps the same request_ref.
provider_typeNoFilter to one provider type — the audited classification dimension (same as the website type chips and AEPI facets); result labels always match this filter. Omit for all types.
max_monthly_usdNoDrop providers whose observed lowest paid tier exceeds this (USD/month). Providers with no observed public price still pass unless require_public_price is true.
require_public_priceNoOnly return providers with an observed public price (default false)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / request_ref
      Added value: +{
+  "description": "Optional task reference (rq_ + 12 hex) from an earlier answer. Continues that task: its remembered facts (quantities, budget, requirements, benchmark selection) apply to this call, and the answer keeps the same request_ref.",
+  "type": "string"
+}
  2. Changed1 schema field changed
    • addedInput schema / properties / deployment
      Added value: +{
+  "description": "Optional (1.10.0). Keep only providers whose EVIDENCED deployment model matches: hosted = a vendor-published MCP endpoint exists (connect, nothing to install); self_hosted = a published install line (npx/uvx/docker/pip/git clone) and no vendor endpoint; both = both are documented; unknown = no evidence held. 'both' listings satisfy hosted and self_hosted. Evidence comes from the vendor's own pages and source directories (Glama/MCP registry attributes) — never guessed from a repository's existence.",
+  "enum": [
+    "hosted",
+    "self_hosted",
+    "both",
+    "unknown"
+  ],
+  "type": "string"
+}
  3. Changed1 schema field changed
    • changedInput schema / properties / provider_type / enum
      Previous value: -[
-  "provider",
-  "mcp",
-  "api",
-  "platform",
-  "infrastructure"
-]New value: +[
+  "agent",
+  "mcp",
+  "api",
+  "platform",
+  "infrastructure"
+]
  4. Changed2 schema fields changed
    • changedInput schema / properties / query / description
      Previous value: -"Free-text capability query, e.g. 'customer support provider with Zendesk integration'"New value: +"REQUIRED. Free-text description of the product job you are buying, e.g. 'customer support provider with Zendesk integration' or 'supplier invoice reconciliation'. Not for questions about Agentery itself (upvotes, endpoints): those return status unsupported_request."
    • addedInput schema / required
      Added value: +[
+  "query"
+]
  5. Changed1 schema field changed
    • addedInput schema / properties / provider_type
      Added value: +{
+  "description": "Filter to one provider type — the audited classification dimension (same as the website type chips and AEPI facets); result labels always match this filter. Omit for all types.",
+  "enum": [
+    "provider",
+    "mcp",
+    "api",
+    "platform",
+    "infrastructure"
+  ],
+  "type": "string"
+}
  6. Changed2 schema fields changed
    • changedInput schema / properties / query / description
      Previous value: -"Free-text capability query, e.g. 'customer support agent with Zendesk integration'"New value: +"Free-text capability query, e.g. 'customer support provider with Zendesk integration'"
    • changedInput schema / properties / require_public_price / description
      Previous value: -"Only return agents with an observed public price (default false)"New value: +"Only return providers with an observed public price (default false)"
  7. Changed3 schema fields changed
    • changedInput schema / properties / billing / description
      Previous value: -"Only agents with one of these observed billing models, e.g. [\"free\",\"freemium\",\"subscription\",\"usage\"]"New value: +"Only providers with one of these observed billing models, e.g. [\"free\",\"freemium\",\"subscription\",\"usage\"]"
    • changedInput schema / properties / max_monthly_usd / description
      Previous value: -"Drop agents whose observed lowest paid tier exceeds this (USD/month). Agents with no observed public price still pass unless require_public_price is true."New value: +"Drop providers whose observed lowest paid tier exceeds this (USD/month). Providers with no observed public price still pass unless require_public_price is true."
    • changedInput schema / properties / sort / description
      Previous value: -"match (default) or price_asc (cheapest observed price first; unpriced agents last)"New value: +"match (default) or price_asc (cheapest observed price first; unpriced providers last)"
  8. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already establish readOnly/non-destructive/openWorld=false, and the description adds substantial behavior beyond that: the exact result shape (fit label with named mismatch, delivery type, price status), the semantics that 'unknown is never free', per-result requirement verification with unverified items marked unknown, and the no_match honesty guarantee. That is the kind of context an agent cannot derive from structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The trigger and the primary workflow routing are front-loaded, and the return-shape sentence is dense but information-bearing. It is longer than strictly necessary for a search tool and could be trimmed, but nearly every clause carries a distinct operational fact.

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?

With no output schema and 10 parameters including a nested filters object, the description carries the return-format burden and does so fully: ranked candidates, fit labels, price status, links, and the no_match case. Combined with 100% schema coverage on inputs, an agent has everything needed to call and interpret this tool.

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 the baseline is 3: enums, ranges and defaults are all documented in the schema itself. The description reinforces a few semantics (requirement checking per result, price-status meaning) but adds little parameter-level detail the schema does not already carry.

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 states a specific verb and resource (search/shortlist AI products, agents, MCP servers, APIs) and gives concrete example queries, which makes it immediately distinguishable from siblings like get_provider_profile (deep dive) and compare_providers (comparison). It also scopes the input domain ('product job you are buying') and explicitly excludes Agentery meta-questions.

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?

Opens with an explicit trigger ('Use when someone wants to find or shortlist...') and then routes downstream: 'open strong ones with get_provider_profile and compare finalists with compare_providers.' It also states the thin-coverage behavior (no_match, never padding), so the agent knows the failure mode rather than inventing results.

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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