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TIGERwu0118

YRB-HES Evidence MCP

by TIGERwu0118

search_evidence

Search evidence in Yellow River Basin human–earth systems reviews, with filters for year, language, and document IDs. Uses lexical subchunk recall and event expansion for precise results.

Instructions

Search YRB3 evidence with exact if_yrbhg gate, lexical Subchunk recall, Event expansion and spatiotemporal attachment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum Subchunk hits
queryYesResearch question or terms
year_toNoOptional publication year upper bound
languageNozh, en, or empty
year_fromNoOptional publication year lower bound
request_idNoOptional audit request ID
document_idsNoOptional exact document IDs
evidence_year_toNoOptional study-evidence year upper bound; overlaps temporal value_start/value_end
evidence_year_fromNoOptional study-evidence year lower bound; overlaps temporal value_start/value_end

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description carries the full burden of disclosing behavior. It mentions internal mechanisms like 'exact if_yrbhg gate' and 'lexical Subchunk recall', which suggest filtering and retrieval behavior, but it does not explicitly state safety (read-only), limitations, or side effects. Some behavioral context is added but not enough for full transparency.

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 a single, concise sentence that front-loads the primary action ('Search YRB3 evidence') followed by qualifying features. No redundant or filler words are present, making it highly efficient.

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

Completeness2/5

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

Despite having an output schema and fully-described parameters, the description fails to explain how this tool fits into the broader context of sibling tools or when to choose it over alternatives. The cryptic terms (YRB3, if_yrbhg) are not defined, leaving gaps in the agent's ability to assess completeness.

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. The description does not add any extra meaning to parameters beyond what the schema already provides. For a search tool, the schema descriptions are clear (e.g., 'Maximum Subchunk hits', 'Research question or terms').

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?

The description uses a specific verb 'Search' and resource 'YRB3 evidence', making the core purpose clear. It also hints at distinct mechanisms (exact if_yrbhg gate, lexical Subchunk recall, event expansion, spatiotemporal attachment) that differentiate it from general search, though the jargon is not explained.

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?

The description provides no guidance on when to use this tool versus its siblings (e.g., search_entity, get_spatiotemporal_evidence). There are no explicit recommendations, exclusions, or alternative references, leaving the agent to infer usage 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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