Skip to main content
Glama
k-rister

ephemeral-buffer

by k-rister

capture_text

Ingest existing text into an ephemeral in-memory buffer and return capture metadata, enabling later hybrid BM25 and semantic search with line-level context.

Instructions

Ingest already-collected text and return capture metadata.

Use this when the caller already has output to index. For a noisy or potentially long command, use execute_and_capture so output remains bounded before it reaches the agent context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelNo
contentYes
content_typeNoauto

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the behavioral burden. It does state the core behavior: ingest text and return capture metadata. However, it does not disclose side effects such as persistence, overwriting, size limits, or auth needs, which would be relevant for a write-like ingest operation.

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?

Two concise, front-loaded sentences. The first gives the action and output; the second provides routing. There is no filler or redundant restating of the tool name or schema fields.

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?

For a tool with only one required parameter and default values on the others, the use case is reasonably clear, and the output schema covers return semantics. Still, with zero annotations and zero schema descriptions, the lack of explanation for label/content_type and the absence of lifecycle or limit details keep it from being fully complete.

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%, so parameter meaning must come from the description. The phrase 'already-collected text' clarifies the role of 'content', but 'label' and 'content_type' are left entirely unexplained, including how 'auto' behaves. This is only partial compensation for an undocumented schema.

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 states a specific verb ('Ingest') and resource ('already-collected text'), and says what is returned (capture metadata). It also distinguishes itself from execute_and_capture by naming that sibling, so an agent can tell the two 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?

It explicitly says when to use the tool ('when the caller already has output to index') and gives concrete direction to use execute_and_capture instead for noisy or potentially long commands. This is clear routing guidance between siblings.

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