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Query a knowledge base with logical reasoning to find all provable answers. Use ?-prefixed variables for unknowns, apply rules and match facts. Optionally retrieve full proof chains. Supports confidence thresholds and scope filtering.

Instructions

Query the knowledge base using multi-step logical reasoning (backward chaining with unification). Finds all provable answers by applying rules and matching facts. Use ?-prefixed variables for unknowns; optionally returns full proof chains. Side effects: none (read-only). Auth: requires X-Tenant-ID header; FACT_READ permission when auth is enabled. Rate-limited per principal. Errors: VALIDATION_ERROR on bad args; result set bounded by INFERENCE_MAX_RESULTS (default 10,000) to prevent OOM.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
predicateYesWhat you're asking about (e.g., 'grandparent', 'can_access')
argsYesUse ?x, ?who for unknowns, concrete values to constrain (e.g., ['?who', 'charlie'])
scopeNoOptional scope filter — omit to query all scopes
withProofNoIf true, include the full reasoning chain showing how each answer was derived (fact matches and rule applications)
minConfidenceNoMinimum confidence threshold 0.0–1.0. Filters out facts and derivations below this confidence.
Behavior5/5

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

Exhaustively discloses side effects (none, read-only), auth requirements (X-Tenant-ID, FACT_READ permission), rate limits, error types (VALIDATION_ERROR), and result bounding (INFERENCE_MAX_RESULTS). Completely compensates for missing annotations.

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?

Well-structured with front-loaded purpose followed by essential usage, side effects, and constraints. Every sentence adds value, though slightly verbose; could be trimmed slightly without losing meaning.

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

Completeness3/5

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

Despite comprehensive behavioral info, the description lacks an explanation of the return format (e.g., JSON structure of results and proof chains). For a complex reasoning tool, this is a notable gap, especially with no output schema provided.

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?

With 100% schema coverage, the description adds marginal value by clarifying the use of ?-prefixed variables for unknowns and optional scopes. This extra context justifies a score above baseline 3.

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?

Clearly states the tool queries a knowledge base using multi-step logical reasoning, distinguishing it from sibling tools like 'context' or 'predicates'. Specific verb and resource, with explicit mention of backward chaining and unification.

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?

Provides context for when to use the tool (querying with logical reasoning), includes instructions on ?-prefixed variables, optional proof chains, and result bounding. Lacks explicit comparisons to alternative siblings for simpler queries, but still offers practical usage notes.

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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