Skip to main content
Glama
knaisoma

data-olympus MCP server

KB Propose Edit

kb_propose_edit
Destructive

Propose edits to markdown files in a governance-grade knowledge base. High-confidence proposals auto-commit; low-confidence ones enter a pending queue for operator review.

Instructions

Propose an edit to an existing (or new) markdown file under an indexed tier. High confidence auto-commits + queues for push; low confidence enters the pending queue for operator review.

evidence: optional supporting-context strings (max 10 items, 500 chars each), persisted in pending meta / audit events and surfaced by kb_pending (not rendered into the postimage: unlike kb_propose_memory, the postimage here is caller-supplied verbatim).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonYesShort reason for the proposed change.
evidenceNoOptional supporting evidence strings, max 10 items of 500 chars each.
postimageYesComplete markdown file content after the proposed edit.
confidenceYesCaller confidence in the proposal, from 0.0 to 1.0.
base_commitYesGit commit the proposal was based on.
target_pathYesKB-relative markdown path to create or edit.
base_blob_shaYesOptional git blob sha for compare-and-swap protection.
agent_identityYesHuman-readable agent identity for audit events.
source_sessionYesStable id of the agent session making the call.
target_file_hashYesOptional content hash for compare-and-swap protection.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

The description discloses key behaviors: auto-commit vs. queuing, evidence handling, and that the postimage is caller-supplied verbatim. Annotations indicate destructiveHint=true and readOnlyHint=false, which align. Additional details about evidence persistence and surfacing via kb_pending add value beyond the annotations.

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 relatively concise at three sentences, with the main purpose stated upfront. The second sentence is somewhat lengthy but packs essential information. It could be slightly more structured, but overall it's efficient.

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?

Given the tool's complexity (10 params, 9 required) and the presence of an output schema (not shown but indicated as present), the description covers the main aspects: purpose, usage guidelines, behavioral details, and parameter semantics. It is sufficiently complete for an agent to invoke correctly.

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%, but the description adds context for the 'evidence' parameter: max 10 items, 500 chars each, persisted in pending meta/audit events, and not rendered into the postimage (unlike kb_propose_memory). This enhances understanding beyond the schema's basic descriptions.

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 clearly states the purpose: 'Propose an edit to an existing (or new) markdown file under an indexed tier.' It uses a specific verb ('propose an edit') and resource ('markdown file'), and the mention of 'indexed tier' differentiates it from siblings like kb_propose_memory.

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?

The description provides explicit guidance on when to use: 'High confidence auto-commits + queues for push; low confidence enters the pending queue for operator review.' It also contrasts with kb_propose_memory by clarifying that evidence is not rendered into the postimage, helping decide between the two tools.

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

Install Server

Other Tools

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/knaisoma/data-olympus'

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