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personalbrain-mcp

by io.github.lonniev

brain_get_thought_graph_paginated

Cursor-based paginated traversal of a thought's connections. Requires npub for credit billing.

⚠️ NOT AUTHORITATIVE FOR RECENT CHANGES. Same cached graph layer as get_thought_graph (upstream: TheBrainTech/thebrain-api-quickstart-python#2) — lags writes by hours-to-days and does not reflect updates or deletes. Use for traversal/ID discovery, never as read-after-write verification; confirm mutations by ID with get_thought.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
npubNoRequired. Your Nostr public key (npub1...) for credit billing.
cursorNoPagination cursor from a previous response
brain_idNoThe ID of the brain (uses active brain if not specified)
directionNo"older" (newest first) or "newer" (oldest first)older
page_sizeNoNumber of results per page (default 10)
dpop_tokenNo
thought_idYesThe ID of the thought
relation_filterNoFilter by relation: "child", "parent", "jump", "sibling"

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior5/5

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

The description reveals critical behavioral traits: the cached layer lags writes by hours-to-days, does not reflect updates/deletes, and requires npub for credit billing. With no annotations provided, this disclosure is essential and well-handled.

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 concise and well-structured: a one-sentence purpose, a one-sentence requirement, then a clear warning block. Every sentence provides value without redundancy.

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 the output schema exists and the input schema has high coverage, the description covers all necessary context: purpose, usage constraints, data freshness limitations, and billing requirement. It is fully sufficient for an agent to select and invoke the tool correctly.

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

Parameters3/5

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

Schema description coverage is 88%, so parameters are well-documented in the schema. The description adds minimal parameter-specific meaning beyond noting npub is required and that pagination is cursor-based, which is already implied by the schema fields.

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 tool performs 'Cursor-based paginated traversal of a thought's connections', which is specific and distinguishes it from non-paginated get_thought_graph. It also clarifies it is for traversal/ID discovery, not authoritative verification.

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?

Explicitly states when to use ('traversal/ID discovery') and when not to ('never as read-after-write verification'), with a clear alternative ('confirm mutations by ID with get_thought'). This provides strong guidance beyond the schema.

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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TDQS

B3.3/5.0
Disambiguation2/5

Multiple tools have overlapping purposes. For example, `brain_request_credential_channel` and `brain_request_patron_credentials` serve similar roles, and `brain_receive_credentials`, `brain_receive_npub_proof`, and `brain_receive_patron_credentials` all handle receiving data from a courier flow. While descriptions help, the sheer number of tools (83) with similar-sounding purposes (check_ vs get_ vs request_ vs receive_ prefixes) makes it hard to quickly distinguish which tool to use.

Naming Consistency3/5

The tools mostly follow a `brain_verb_noun` pattern (e.g., `brain_create_thought`, `brain_delete_link`), which provides some consistency. However, there are inconsistencies with prefixes like `brain_oracle_` (e.g., `brain_oracle_about`, `brain_oracle_how_to_join`) which are more like static pages than actions. Additionally, 'check' and 'get' seem interchangeable (e.g., `brain_check_balance` vs `brain_get_thought`), and 'list' is used alongside 'get' in a way that sometimes means the same thing (e.g., `brain_list_brains` vs `brain_get_brain`).

Tool Count2/5

83 tools is an extremely large and unwieldy surface area. While the server aims to be a comprehensive 'operating system' for a specific ecosystem (DPYC/Nostr), this many tools will lead to agent confusion and high latency. Tools like `brain_oracle_about`, `brain_oracle_how_to_join`, and `brain_oracle_network_advisory` could easily be combined into a single tool or served as function parameters.

Completeness4/5

For its stated domain (managing a 'brain' with credits, payments, and Nostr integration), the tool set is remarkably complete. It covers CRUD operations, payment flows (purchase, check, restore), coupon management, credential handling, and even notarization. Minor gaps are hard to identify, though some flows feel overly complex (e.g., the multiple `request_`/`receive_` patterns could arguably be simplified). The high number of tools is a result of this extreme specialization.

Resources