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Memory MCP

Memory MCP

by mrunmayeekokitkar
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

get_project_context

Load a structured, category-grouped snapshot of a project's purpose, architecture, modules, workflows, decisions, and status at session start to ground AI context with complete project knowledge.

Instructions

Get a structured snapshot of everything known about the project.

Call this at the START of a session to load the full project model into your context — purpose, architecture, modules, workflows, decisions, domain terms, and status — grouped by category. Much richer than recall which returns a flat ranked list for a specific query.

How to resolve the project_name

Same as remember: auto-match if unambiguous. If ambiguous, call list_projects() and explicitly ask the user to choose. Once chosen, session-lock it.

Args: project_name: Project name to search within. Also includes global memories. Omit for global-only. categories: Optional filter — only include specific categories. Valid values: "identity", "stack", "project", "module", "workflow", "decision", "domain", "status". Pass null/omit for all categories. detail_level: "full" (complete content) or "summary" (first sentence only, for lightweight injection when context is tight). tool: Calling tool name (for audit).

Returns a dict keyed by category, each containing a list of facts with their content, key, and importance. Only non-empty categories appear.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_project_context"
  }
}

Input Schema

NameRequiredDescriptionDefault
toolNounknown
categoriesNo
detail_levelNofull
project_nameNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and mostly succeeds: it discloses the return shape (dict keyed by category, only non-empty categories), the summary-vs-full cost tradeoff, and the fact that a project choice gets session-locked. The gap is that 'session-lock it' is a state change whose effect is never explained, and read-only/non-destructive nature is only implied by 'Get'.

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?

Front-loaded with the purpose and the start-of-session directive before any detail, and the Args/Returns sections are scannable. Slightly longer than needed — the return-format paragraph partially duplicates the output schema, and the `remember` cross-reference costs a sentence.

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?

For a four-parameter, zero-annotation read tool, everything an agent needs is present: when to call, how to resolve the project, what the category enum is, the detail-level cost tradeoff, and the result structure. The output schema exists, so the Returns section is a bonus rather than a gap-filler.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate entirely, and it does: `categories` gets the full valid-value enum that the schema omits entirely, `detail_level` gets both allowed values with their semantic tradeoff, `project_name` gets the omit-for-global-only rule, and `tool` is explained as an audit field.

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?

States a specific verb and resource ('Get a structured snapshot of everything known about the project') and enumerates the content domains it covers (purpose, architecture, modules, workflows, decisions, domain terms, status). It explicitly contrasts itself with the `recall` sibling, so an agent can choose between them without opening either schema.

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?

Gives an explicit trigger ('Call this at the START of a session'), names the alternative it is not (`recall`, which returns a flat ranked list for a specific query), and routes the ambiguity case to `list_projects()` with a user prompt. When-to-use, when-not-to-use, and the fallback path are all present.

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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