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Answer coding questions by retrieving and semantically filtering web sources, returning compact evidence with provenance instead of raw pages.

Instructions

Research a coding question and return compact evidence with provenance.

Args: query: The coding/research question (non-empty, max 2000 chars). max_sources: Max sources to fetch (1..server cap, default server-side). max_evidence_tokens: Evidence budget cap (approx tokens, whole blocks only). mode: 'jev' (semantic filtering) or 'baseline' (no JEV, for comparison).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNojev
queryYes
max_sourcesNo
max_evidence_tokensNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It does add real context — mode semantics ('jev' = semantic filtering, 'baseline' = no JEV) and that the token budget truncates to whole blocks — but says nothing about cost, latency, network/auth requirements, or whether it is a read-only operation.

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 one-line purpose is front-loaded, followed by a tight per-argument list. It is slightly redundant with the schema's parameter names, but every line adds a constraint or default rather than restating a type.

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 values need not be described, and all four parameters are covered with constraints and defaults. The remaining gap is usage/selection guidance, which is captured in its own dimension rather than blocking correct invocation.

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 coverage is 0%, so the description must compensate and largely does: it supplies the query length limit (max 2000, non-empty), the max_sources range (1..server cap) and server-side default, the token-budget semantics (approx tokens, whole blocks only), and the two valid mode values, none of which appear in the schema.

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 and resource ('Research a coding question') plus the output shape ('compact evidence with provenance'). No siblings exist to differentiate from, so a 4 is the ceiling here.

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

Usage Guidelines2/5

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

There is no when-to-use, when-not-to-use, or alternative-tool guidance. The only hint is that 'baseline' mode exists 'for comparison', which implies A/B usage but never states when an agent should prefer one mode over the other.

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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