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ESG Hub MCP Server

Search ESG (hybrid)

search_content
Read-only

Hybrid search that fuses 384-dim semantic similarity with BM25 and ESG re-ranking across ESG Hub articles and external resources. Use for conceptual or paraphrased questions — natural-language phrases work better than single tokens because query is embedded; when the user gives an exact identifier or phrase prefer the cheaper search_esg. query must be non-empty and limit defaults to 10 and is capped at 50. Returns one ranked page of up to limit items, each carrying a fused relevance score (higher is better); there is no pagination, so raise limit to widen, and unlike search_esg there is no source filter. Zero matches returns an empty item list, not an error. Cached ~2 minutes; rate-limited per IP; retry on 5xx.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results
queryYesSearch query (e.g., 'carbon emissions', 'board diversity')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
countYes
itemsYes
queryYes
totalYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only provide readOnly/openWorld hints; the description adds substantial extra context: no pagination, ranked single page with fused scores, empty-list-on-zero-matches, ~2-minute caching, per-IP rate limiting, and 5xx retry guidance. That is rich operational disclosure beyond the structured fields.

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?

Dense but front-loaded and well ordered: purpose, routing, then constraints and operational notes. A couple of facts (limit default 10, cap 50) duplicate the schema, minor redundancy for an otherwise efficient passage.

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?

An output schema exists so return-value detail is optional, yet the description still explains the ranked page, score semantics and empty-list behavior. Combined with caching/rate-limit notes and the search_esg contrast, nothing an agent needs to call this correctly is missing.

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 100%, so the baseline is 3; however, the description adds genuine meaning not in the schema — that `query` is embedded (so NL phrases beat single tokens) and that `limit` is capped at 50 with no pagination to widen results. It slightly repeats the schema's default/cap, keeping it from a 5.

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?

States a specific verb+resource (hybrid search over ESG Hub articles and external resources) and details the fusion mechanism (384-dim semantic + BM25 + ESG re-ranking). It is immediately distinguishable from the sibling search_esg, which it names explicitly.

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

Usage Guidelines5/5

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

Gives explicit when-to-use ('conceptual or paraphrased questions', natural-language phrases) and when-not ('exact identifier or phrase prefer the cheaper search_esg'), with the reason (query is embedded). This is textbook alternative routing.

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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