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Get entity context

get_entity_context
Read-onlyIdempotent

Reads the current recommendation rationale, evidence, rule meaning and provenance for a named product or supplier. It does not read past decisions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entity_nameYesEntity name exactly as the user wrote it.
entity_typeNoWhether entity_name is a supplier or a product; omit to match either.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYesMatching lines with their recommendation rationale, or catalogue rows when the snapshot had none.
scopeYes
truncatedYesTrue when more rows matched than were returned.
evidence_idYesHandle for these rows within this call's results. Not a citation.
returned_rowsYesRows in this response.
source_evidence_idsYesWorkspace evidence IDs backing these rows. Empty when the source carries no evidence records.
total_matching_rowsYesRows that matched before any limit.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered structurally. The description adds one genuinely non-redundant behavioral boundary, the temporal scope ('current' only, 'does not read past decisions'), but says nothing about staleness, refresh, or scoping failures. Useful but thin added context.

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?

Two short sentences, the affirmative scope front-loaded and the negative scope immediately after. No filler, no repetition of the title or name.

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?

An output schema exists, so return-value structure need not be described, and annotations cover the safety profile. For a two-parameter read-only tool the description is essentially complete; only the ambiguity against get_entity_profile keeps it short of full marks.

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 both parameters (entity_name and the entity_type enum) are already fully documented, including the 'omit to match either' behavior. The description only echoes 'a named product or supplier' and adds no syntax or matching semantics beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Reads') and a concrete set of resources ('recommendation rationale, evidence, rule meaning and provenance') scoped to a named product or supplier. An agent can tell it retrieves context rather than raw master data. However, it does not differentiate itself from the nearby sibling get_entity_profile, which an agent could easily confuse it with.

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

Usage Guidelines3/5

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

Usage is only implied by the resource description; the one explicit constraint is the exclusion 'It does not read past decisions.' No positive when-to-use guidance and no named alternative (e.g. a history or decisions tool) is offered, so the agent must infer selection from context.

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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