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selvin-search-mcp

selvin-search-mcp

by selvinchen97-lab
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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plan_search_term

Submit a batch of search terms with purpose references and strategy-level controls for approach and fallback.

Instructions

Phase 4: Submit ALL search terms in ONE call (batch).

terms_json: JSON array, each element shape: {"term":"react server components 2025","purpose":"sq1","round":1}

Strict rules (enforced):

  • term MUST be ≤8 words (engine rejects otherwise)

  • purpose MUST reference a declared sub-query id

  • one term per (purpose, round); add multiple rounds for follow-up refinement

approach (broad_first | narrow_first | targeted) and fallback_plan are strategy-level; pass them as top-level params, not inside terms.

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": {
      "session_id": "<session_id>",
      "thought": "<thought>",
      "terms_json": "<terms_json>"
    },
    "name": "plan_search_term"
  }
}

Input Schema

NameRequiredDescriptionDefault
thoughtYesReasoning for the full strategy
approachNobroad_first | narrow_first | targetedtargeted
confidenceNoConfidence 0.0-1.0
session_idYesSession ID from plan_intent
terms_jsonYesJSON array of search-term objects (see description)
is_revisionNoTrue to replace prior strategy
fallback_planNoFallback if primary searches fail

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses key behavioral constraints (term length, purpose reference, one term per purpose/round) and clarifies that approach and fallback_plan are strategy-level. Some behaviors like error handling or side effects are absent, but the description is sufficiently transparent given the complexity.

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 concise: a one-line summary followed by structured bullets. It front-loads the main purpose and delivers key rules efficiently. No unnecessary words or repetition.

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

Completeness3/5

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

The description thoroughly covers the core parameter (terms_json) but does not explain the purpose of other parameters like thought, confidence, or is_revision beyond what the schema provides. Given the tool's role in a workflow, some of these may be self-explanatory, but the description could add more context.

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 baseline is 3. The description adds value by specifying the exact shape of terms_json (object with term, purpose, round) and rules. It also clarifies that approach and fallback_plan are top-level params, not inside 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 clearly states the tool's purpose: submit all search terms in one batch call (Phase 4). It uses specific verbs and resources, but does not explicitly differentiate from sibling tools like plan_sub_query or plan_intent.

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?

The description gives implicit usage context (Phase 4, batch submission) and provides rules for terms (≤8 words, purpose must reference sub-query id). However, it lacks explicit guidance on when not to use the tool or when alternatives are preferred.

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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