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jagoff

MEMO MCP Server

by jagoff

memo_graph

Read-onlyIdempotent

Discover and explain entity connections in the knowledge graph: shortest paths, weighted evidence routes, neighbors, communities, and code impact.

Instructions

Navigate the entity knowledge graph (read-only).

One consolidated explorer over memo's corpus graph. Pick a verb:

  • "path": shortest entity path from a to b (fewest hops).

  • "why": weighted shortest path a->b as evidence — the same route with each hop's edge weight (how many memories bridge it), so a connection is explained, not just asserted.

  • "neighbors": direct neighbours of entity (or a).

  • "explore": a rich "what's around X" view of entity (or a) — degree, neighbours, and the memories that mention it.

  • "communities": clusters of related entities (limit caps count).

  • "impact": changed code plus linked durable memories for cwd.

  • "architecture": a bounded CodeContextPack for cwd. entity (or a) selects a symbol/path focus; mode is scout, verify, or audit and cursor continues an incomplete page.

By default this navigates the MEMORY graph only (entities linked through shared memories). Set include_code=True to also fold in the codegraph code-structure layer (call/extends/etc. edges between code symbols).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aNoFirst entity (path/why source; fallback for entity).
bNoSecond entity (path/why target).
cwdNoGit working tree used by the impact verb.
modeNoArchitecture evidence mode: scout | verify | audit.scout
verbYesOne of path | neighbors | explore | communities | why | impact | architecture.
depthNoCodeGraph hop depth for impact (bounded to 0..3).
limitNoResult cap (neighbours, mentioning memories, communities).
scopeNoRepo-relative path bounding architecture findings.
cursorNoOpaque continuation cursor returned by architecture.
entityNoEntity name for neighbors/explore.
max_charsNoApproximate architecture finding budget.
include_codeNoFold in the codegraph code-structure layer (default off → memory-only, so results are durable-memory navigation).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations already declare read-only, idempotent, and non-destructive hints. The description adds substantial behavioral context: the memory-only default, how include_code folds in the codegraph, the meaning of 'why' as a weighted path, and cursor-driven pagination for architecture. This goes beyond the annotations and clearly explains what the tool returns and how it behaves.

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 thorough but well-structured: a one-line summary followed by a bulleted list of verbs with concise explanations. It is front-loaded with the main purpose and no sentence is redundant. Despite its length, it is appropriately sized for the tool's complexity.

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?

Given 12 parameters, 7 verbs, and an existing output schema, the description covers all operational aspects: verb definitions, parameter roles, default behavior, include_code toggle, pagination via cursor, and limits. The output schema provides return details, so the description is complete for effective tool selection and invocation.

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?

The schema already describes all parameters, so the baseline is 3. The description adds value by mapping verbs to parameters (e.g., 'path' uses a and b, 'impact' uses cwd, 'neighbors/explore' use entity or a) and by explaining the semantics of include_code and cursor. This is more than the schema alone provides.

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 clear verb+resource statement: 'Navigate the entity knowledge graph (read-only).' It then enumerates specific sub-operations (path, why, neighbors, explore, communities, impact, architecture), which distinguishes it from sibling tools like memo_search or memo_get. The scope is very clear.

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

Usage Guidelines4/5

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

The description provides clear context for each verb and explains the default memory-only behavior with include_code to add the codegraph layer. It does not explicitly name alternative tools or when not to use it, but the context is strong enough to infer its intended use-case.

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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