Skip to main content
Glama
thammarongg

blueocean-vector

by thammarongg

memory_search

Search project memories with semantic queries. Retrieve relevant decisions and context as summarized and full layers, while controlling token limits for efficient access.

Instructions

Semantic search a project's memory with token-budgeted return (summary + full layers).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
areaNo
queryYes
top_kNo
moduleNo
projectYes
max_tokensNo
importance_minNo
time_range_endNo
time_range_startNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

C2.6/5.0
Behavior2/5

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

No annotations exist, so the description must carry the full burden. It hints at token budgeting and a two-layer result structure, but it doesn't disclose what happens when the token budget is exceeded (truncation? sorting?), whether the tool is idempotent/read-only, or any rate-limit concerns. For a semantic search that returns multiple entries, key behavioral traits like ordering (by relevance?) and result format are underspecified.

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 a single, front-loaded sentence with no wasted words. It could improve clarity by unpacking jargon like 'full layers' and 'token-budgeted return', but it remains concise and scannable. A small structure improvement would be breaking into two sentences for readability.

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

Completeness1/5

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

Given the high complexity (9 parameters, zero descriptions, no output schema), a one-line description is vastly insufficient. It omits the return format structure, ordering, how filters combine, and the role of the token budget. The agent cannot reliably determine how to craft calls or interpret results from this definition alone.

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

Parameters2/5

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

Schema description coverage is 0% for the nine parameters, so the description must compensate. It mentions 'query' and 'token-budgeted' (mapped to max_tokens) but fails to explain area, module, top_k, importance_min, time_range_start, time_range_end, or their interplay. Most parameters remain opaque to the agent, making effective invocation difficult.

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 identifies the tool as performing a semantic search over a project's memory with a token-budgeted return that includes both a summary and full layers. This distnguishes it from siblings like memory_get (exact retrieval) and memory_store (write), but it doesn't explicitly contrast them. The purpose is well-stated but could be strengthened by naming when to prefer this over other memory tools.

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

Usage Guidelines2/5

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

The description gives no guidance on when to use semantic search vs. the alternative memory tools like memory_get or memory_list_projects. It doesn't state prerequisites, restrictions (e.g., project must exist), or typical use cases. With seven siblings, explicit 'when to use' advice is missing.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/thammarongg/blueocean-vector'

If you have feedback or need assistance with the MCP directory API, please join our Discord server