Skip to main content
Glama

ESG Hub MCP Server

Search ESG (keyword)

search_esg
Read-only

Exact keyword (BM25) search across ESG Hub articles and curated external resources. Use when the user supplies a specific term, identifier, or phrase (e.g., 'GRI 305', 'Scope 3'); for paraphrased or conceptual questions prefer search_content, which adds semantic similarity. Terms are matched individually, not as one exact phrase, and source narrows to 'pages' (ESG Hub articles) or 'external' (curated third-party URLs). Returns one ranked page of up to limit (max 50) items — there is no pagination, so raise limit to widen; zero matches returns an empty item list, not an error. Reads are cached ~2 minutes and rate-limited per IP; a 5xx means the API is redeploying — retry shortly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results
queryYesSearch query (e.g., 'carbon emissions', 'GRI standards')
sourceNoFilter by source typeall

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
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?

Goes well beyond the readOnlyHint/openWorldHint annotations by disclosing matching semantics (terms matched individually, not as a phrase), the absence of pagination and how to widen results, empty-result behavior (empty list, not an error), ~2 minute read caching, per-IP rate limiting, and 5xx-means-redeploy retry guidance. This is exactly the operational context annotations cannot carry.

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?

Front-loaded with purpose and the sibling contrast before operational details, and nearly every clause carries distinct information. It is dense and slightly long as a single block, but there is little true filler.

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?

With an output schema present, return shape need not be described, and the description still covers matching semantics, result volume/pagination, empty results, caching, rate limits, and failure modes. Nothing an agent needs to invoke and interpret this tool 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, but the description adds real semantics the schema lacks: what 'pages' vs 'external' actually mean for `source`, and that `limit` caps at 50 with no pagination (raise limit to widen). The query-matching behavior is also clarified. It stops short of full per-parameter treatment, so not 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 and mechanism (exact keyword/BM25 search) over a named resource (ESG Hub articles and curated external resources), and explicitly contrasts itself with the sibling search_content. An agent can distinguish it from search_content without opening either schema.

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 an explicit when-to-use trigger (user supplies a specific term, identifier, or phrase, with examples 'GRI 305', 'Scope 3') and an explicit when-not with the named alternative (paraphrased/conceptual questions → search_content, which adds semantic similarity). Nothing is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.