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Python
Hybrid

Delimit Memory Search

delimit_memory_search
Read-onlyIdempotent

Search past conversations semantically to retrieve decisions and context across sessions. Ask natural-language queries to find relevant memories.

Instructions

Search conversation memory semantically (Pro).

When to use: to recall prior context by meaning rather than recency — e.g. "what did we decide about deploys?" finds relevant entries across sessions. When NOT to use: for the chronological tail (use delimit_memory_recent) or to write a memory (delimit_memory_store).

Sibling contrast: delimit_memory_recent is the free chronological tail; this is the Pro semantic search.

Side effects: read-only on the memory backend; gated by require_premium. Calls backends.memory_bridge.search.

Prerequisite: requires Delimit Pro. An unlicensed call returns {"error": ..., "upgrade": "https://delimit.ai/pricing"} without running.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of matching entries to return. Default 10.
queryYesNatural-language search query. Required.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv4.7.9
    • changedInput schema / properties / limit / description
      Previous value: -"Maximum results to return."New value: +"Maximum number of matching entries to return. Default 10."
    • changedInput schema / properties / query / description
      Previous value: -"Natural language search query."New value: +"Natural-language search query. Required."
  2. Changed2 schema fields changedv4.5.5
    • addedInput schema / properties / limit / description
      Added value: +"Maximum results to return."
    • addedInput schema / properties / query / description
      Added value: +"Natural language search query."
  3. First observedv0.1.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint/idempotentHint/destructiveHint, and the description adds meaningful context: it is gated by require_premium, calls backends.memory_bridge.search, and documents the unlicensed error/upgrade response. This goes well beyond the structured annotation data and correctly aligns with it.

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?

The description is well-structured with labeled sections and front-loaded purpose. Every sentence earns its place: usage guidance, sibling contrast, side effects, and prerequisite are all covered without redundant repetition of the title or schema.

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?

The description is complete for an agent to select and invoke the tool correctly: it explains when to use it, when not to, what backend behavior to expect, the Pro prerequisite, and the unlicensed failure mode. An output schema exists, so return-value details are not needed here.

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% and both parameters are already documented, so the baseline is 3. The description adds value by clarifying that 'query' is natural-language and meaning-based rather than recency-based, with a concrete example, which helps the agent phrase queries appropriately.

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 opens with a specific verb and resource: 'Search conversation memory semantically.' It clearly distinguishes itself from siblings by naming delimit_memory_recent and delimit_memory_store and explaining what each is for, so an agent can immediately tell them apart.

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?

Explicit 'When to use' and 'When NOT to use' sections give concrete guidance with an example query and name the alternative tools for chronological tail and memory writing. This leaves no ambiguity about when the agent should choose this tool over alternatives.

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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