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Knowledge Fabric

list_sources

List ingested source types and document counts for a tenant to identify available content domains before issuing queries, enabling scoped retrieval by source type.

Instructions

List ingested source types and document counts for a tenant.

Returns distinct source_type values with document counts so an AI agent can discover what content domains are available before issuing a query. Use source_type as a filter in retrieve_evidence to scope retrieval. Scoped to tenant_id when provided.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tenant_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the behavioral burden. It discloses that the tool returns distinct source_type values with counts and that results are scoped to tenant_id when provided. This is enough for a read-only metadata listing, though it does not cover edge cases like empty results or permission requirements.

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 compact and front-loaded with the verb and resource. Every sentence adds necessary context: what is returned, why an agent would call it, how to apply the result, and how the optional parameter behaves.

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?

For a tool with one optional parameter and an output schema, the description is complete. It explains purpose, usage, return content, and parameter behavior, leaving no critical ambiguity for an agent deciding whether and how to call it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It does by explaining that tenant_id scopes the results when provided, adding behavioral meaning beyond the raw schema. It does not detail the parameter type or omission behavior, but the schema and the phrase 'when provided' make the optionality clear.

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 uses a specific verb and resource: it lists ingested source types and document counts for a tenant. It distinguishes itself from retrieval tools by noting the purpose is discovery before querying and names retrieve_evidence as the downstream consumer.

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 clearly states when to use the tool: before issuing a query, to discover available content domains. It also tells the agent how to use the returned source_type values with retrieve_evidence. It does not list exclusions or compare against all siblings, but the context is clear.

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