Lessons Learned Archive

semantic_search

Semantically search COVID-19 pandemic modeling lessons learned, expert recommendations, and scientific findings.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "semantic_search"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes	Search topic or question (e.g., 'school closures impact', 'vaccine prioritization', 'hospital capacity models')
`top_k`	No	Number of top results to return (default: 5, max: 20)

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 8, 2026

TDQS

A3.6/5.0

Behavior3/5

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

No annotations are supplied, so the description carries the full behavioral burden. It does disclose one genuinely useful trait — retrieval is semantic, so natural-language paraphrases work better than keyword strings — but says nothing about ranking, result format, or whether the corpus is fixed and read-only.

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?

A single front-loaded sentence with the verb first and the domain scope immediately after. Nothing is redundant or padded.

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?

The tool is simple — one required string plus a result count — and the fully documented schema covers invocation. The only shortfall is that with no output schema, the description never hints at what a returned record contains (lesson, recommendation, source citation), which would help an agent interpret results.

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% (both `query` and `top_k` including default and max), so the schema already does the explanatory work. The description adds no parameter-level meaning beyond that, which is the baseline-3 case.

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 pairs a specific verb ("semantically search") with a precisely scoped resource: COVID-19 pandemic modeling lessons learned, expert recommendations, and scientific findings. That domain scoping is genuinely differentiating, though with no sibling tools there is no alternative to distinguish from.

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

Usage Guidelines3/5

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

Usage is only implied by the word "search" — an agent can infer it should be called with a topic or question, but the description states no conditions, prerequisites, or exclusions. With no siblings present, the lack of alternative routing is a minor gap rather than a serious one.

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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