brainfile
Server Details
Brainfile memory layer for Claude Code over MCP: free starter files + weekly freshness feed.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
TDQS
Scored across 3 tools
Each tool has a clearly distinct role: list_pack_contents provides the manifest, get_starter_file fetches onboarding files, and get_freshness_sample retrieves a specific feed entry. There is no meaningful overlap between them, and the descriptions reinforce exactly what each returns.
All tool names follow a consistent verb_noun pattern using snake_case: get_freshness_sample, get_starter_file, list_pack_contents. The two 'get_' verbs and one 'list_' verb are a predictable, coherent convention.
Three tools is a well-scoped set for the server's narrow purpose: listing package contents, fetching starter files, and fetching a sample feed entry. Each tool serves a distinct user need, and the count matches the small domain without being bloated.
The server covers the free-tier on-ramp completely: you can see everything in the pack, get the starter files, and get a freshness sample. The only gap is that paid role packs are intentionally locked and cannot be fetched through the server, but that appears to be a deliberate business constraint rather than an oversight.
Available Tools
3 toolsget_freshness_sampleAInspect
Get the real Brainfile freshness feed entry that ships in the pack — the weekly 'living system' update (the same improvements we run on our own six live businesses + what changed in Claude Code, with how to apply each to your brain/). This is the retention mechanic; a paid license keeps the newest feed flowing.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explains the tool retrieves a weekly update about improvements, implying it is read-only and idempotent. The retention mechanic context adds behavioral insight. It does not explicitly state it is a read operation, but the description's content suffices for transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, but the first sentence is quite long and includes specific details about the feed content. The second sentence concisely explains the retention mechanic. Every sentence adds value, but the first could be slightly more streamlined.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters, no output schema, and simple sibling tools, the description is fairly complete. It explains what is returned (the freshness feed entry) and why it exists (retention mechanic). It does not detail the format of the feed, but that is reasonable for a non-technical description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, and schema coverage is 100% (empty schema). The description adds meaning beyond the schema by detailing what the tool returns (the weekly feed entry with specific content), which is valuable context for an AI agent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies it gets the 'real Brainfile freshness feed entry' which is a weekly update. It provides specific content details (improvements to businesses and Claude Code), making the purpose clear. However, it does not explicitly distinguish from sibling tools like 'get_starter_file' or 'list_pack_contents', though the content description implies differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions it is a 'retention mechanic' and that a paid license keeps the newest feed flowing, which hints at when to use. However, it does not provide explicit guidance on when to use this tool versus alternatives, nor does it state any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_starter_fileAInspect
Get the REAL Brainfile free-tier starter files — the on-ramp that turns generic Claude into a project that remembers you. Full text, no account needed. Files: INTERVIEW-ME.md (first-session interview that personalizes your CLAUDE.md), START-HERE.md (orientation + first prompt), CLAUDE.starter.md (starter operating-system playbook). Omit 'name' to get all three.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Which starter file to fetch. Omit for all three. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description adds key traits: 'Full text, no account needed', and lists files. Does not mention rate limits or authentication, but tool is read-only and non-destructive, so adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences; first states purpose, second lists files and usage note. No filler, front-loaded with key info.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Complete for a simple tool: explains what it retrieves, how to use the parameter, and lists contents. No output schema needed given clarity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% with enum, but description adds valuable context: omission returns all three files. This goes beyond the schema's individual file descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb ('get') and resource ('starter files'), names specific files, and explains behavior. Distinguishes from sibling tools like get_freshness_sample.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Indicates when to use (on-ramp for personalization, no account needed) but does not explicitly state when not to use or mention alternatives. However, sibling tools are distinct, making context clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_pack_contentsAInspect
List the full Brainfile pack manifest: the v1 pattern files (Autopilot protocol, never-empty backlog, never-forgets ledger, interview onboarding, end-of-run brief template, conversation-history pattern, freshness feed — each with a one-line description; starter files fetchable right here over MCP, the rest via the free no-card trial pull), plus the paid role packs (15) — listed but locked, with the unlock URL. Use this to see everything Brainfile ships before starting a trial.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that paid role packs are listed but locked with an unlock URL, and that starter files are fetchable via MCP. It transparently describes what the tool includes and excludes, though it could clarify the output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is quite long and enumerates many items (v1 pattern files with descriptions), which adds detail but reduces conciseness. It is front-loaded with the main purpose and structured as a list, but some redundancy exists (e.g., 'listed but locked'). Overall, it is informative but could be more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the high-level content (v1 files with descriptions, paid packs locked) but does not specify the response format (e.g., list, JSON). Since there is no output schema, the description should compensate for format, which it lacks. It is fairly complete for a listing tool but could be clearer about what the agent receives.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so the schema is perfectly covered. The description does not need to add parameter info and does not, which is appropriate. Baseline 4 applies for 0 parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'list' and the resource 'full Brainfile pack manifest', enumerating specific components (v1 pattern files, paid role packs). It distinguishes from sibling tools by indicating it provides a comprehensive overview before starting a trial, whereas siblings retrieve specific files or samples.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use this to see everything Brainfile ships before starting a trial', providing a clear when-to-use context. It implies alternatives (sibling tools) for fetching specific files, though it does not explicitly state when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
3 tool updates
- First observed
get_freshness_sample - First observed
get_starter_file - First observed
list_pack_contents
Related MCP Connectors
Private persistent memory for Claude, ChatGPT & Gemini via MCP - semantic search, zero-code setup.
Persistent AI memory shared across Claude, ChatGPT, coding agents, and compatible MCP clients.
- mcpOAuthai.butlerbrain
Persistent memory for AI assistants. Save once; recall from Claude, ChatGPT, or any MCP client.
Persistent memory for AI agents across Claude, ChatGPT and any MCP client.
Related MCP Servers
- AlicenseAqualityDmaintenanceGives Claude Code, Claude Desktop, Cursor, VS Code Copilot, and other MCP-compatible tools persistent memory.18531MIT
- AlicenseNot gradedqualityDmaintenanceGives AI persistent personal memory with hybrid search, temporal decay, and knowledge graph. Works with Claude and any MCP client.868MIT
- AlicenseBqualityCmaintenanceCross-tool AI memory MCP server for Claude Code, Cursor, Antigravity, Windsurf, and every MCP-compatible AI. 400 tokens not 4,000.34MIT
- AlicenseBqualityBmaintenancePersistent memory for AI agents built on the LLM Wiki pattern: a plain-Markdown brain (also a valid Obsidian vault) with SQLite metadata, local semantic search via fastembed (no API keys), one-call session context with project auto-detection, and a decision log with rationale. Works with Claude Code, Claude Desktop, Cursor, and any MCP client.31MIT