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yharby

source-coop-mcp

by yharby

search

Search across 94+ organizations and all products, published or unpublished, using fuzzy matching that handles typos and partial words, returning top 5 matches.

Instructions

Search for products across ALL accounts with smart fuzzy matching. Handles typos, partial matches, and incomplete words using 60% similarity threshold.

Hybrid Search - Automatically searches across:

  • All 94+ organizations

  • ALL products (published + unpublished)

  • All fields: title, description, product_id

Published products: Full metadata (title, description, product_id) Unpublished products: product_id only (no title/description available)

Args: query: Search keyword (supports typos and partial matches)

Returns: Top 5 matching accounts or products (sorted by relevance score)

Performance: ~5-8s (parallel 2-level S3 scan + top 5 API enrichment)

Performance breakdown:
- S3 parallel listing: ~2.4s (94 accounts + 354 products)
- Fuzzy matching: <1s (in-memory processing)
- API enrichment: ~2-5s (only top 5 results)

**11x faster** than sequential approach (was ~27s)
**Uses 2-level delimiter listing** (not full recursive scan)

Examples: >>> # Exact match >>> results = await search("climate")

>>> # Fuzzy match (handles typos)
>>> results = await search("climte")  # Finds "climate"
>>> results = await search("exiopase")  # Finds "exiobase-3" (includes unpublished!)

>>> # Partial match
>>> results = await search("geo")  # Finds "geospatial", "geocoding", etc.

>>> # Result formats
>>> print(results[0])  # Account match
{
    "type": "account",
    "account_id": "harvard-lil",
    "match_string": "harvard-lil",
    "search_score": 9.5,
    "similarity": 0.95,
    "matched_fields": ["account_id"]
}

>>> print(results[1])  # Product match
{
    "type": "product",
    "account_id": "youssef-harby",
    "product_id": "exiobase-3",
    "match_string": "youssef-harby/exiobase-3",
    "title": "",  # Empty for unpublished products
    "description": "",  # Empty for unpublished products
    "search_score": 8.2,
    "similarity": 0.82,
    "matched_fields": ["product_id"]
}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations provided, the description fully discloses behavioral traits: fuzzy matching with 60% similarity threshold, hybrid search across 94+ organizations and all products, metadata differences for published vs unpublished, performance details (5-8s, 11x faster), and result structure with examples.

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 description is well-structured with sections (details, args, returns, performance, examples) and front-loads the core purpose. While somewhat lengthy, every part adds value (especially the examples). Could be slightly more concise but remains effective.

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?

Given the tool's complexity (fuzzy search across accounts/products, handling unpublished items, performance characteristics), the description covers all aspects thoroughly. It includes output schema details in examples, addresses edge cases (unpublished products), and provides usage guidance, making it complete for effective selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 1 parameter with 0% description coverage, but the description explains 'query' as 'Search keyword (supports typos and partial matches)' and provides multiple examples of its usage. This adds significant meaning beyond the schema definition.

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?

The description clearly states 'Search for products across ALL accounts with smart fuzzy matching', specifying the verb, resource, and key differentiating features like fuzzy matching and cross-account search. It distinguishes from sibling tools that likely do exact listing or detail retrieval.

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?

The description provides extensive guidelines on when to use this tool: for fuzzy searching, handling typos, and partial matches across all accounts and products (published and unpublished). It explains what is searched and the return format but does not explicitly state when not to use it or provide direct comparisons to sibling tools.

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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