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personalbrain-mcp

by io.github.lonniev

brain_list_canonical_identities

Return canonical (tool_id, mcp_name, …) for every tool the wheel exposes.

The authoritative source for any client (Studio, agents, FE) that needs to know how this MCP identifies its tools. Reconcile uses this output to UUID-join against the stored pricing model — no name-based UUID derivation, no guessing.

Includes both ToolIdentity-seeded tools and any UUID recorded by @paid_tool that is missing from the registry. The latter appear with registered: false (and in the top-level unregistered array) so Reconcile can flag deploy drift instead of silently reporting clean when a live tool was never seeded (#174).

If the operator renames a function or rebrands a slug, the mcp_name in this output changes but tool_id stays. That's the whole point of the canonical-UUID design.

Also diffs the live FastMCP wire surface against the registry. Tools exposed on the wire but absent from the registry appear in unregistered so Reconcile can flag deploy drift instead of silently under-reporting (issue #175).

Free, no side effects.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.8/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It explicitly discloses 'Free, no side effects,' explains the two sources of identity (ToolIdentity-seeded and @paid_tool recorded UUIDs), and describes the unregistered/deploy-drift detection behavior including the mcp_name-change-vs-tool_id-stability design point. Very transparent about return semantics.

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?

Slightly long with some redundancy — the unregistered/deploy-drift concept is explained twice (once for @paid_tool UUIDs and again for wire-surface diff). Could be tightened, but every sentence adds meaningful context and the key points are front-loaded in the first line.

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?

Output schema exists, so return format doesn't need explanation. The description thoroughly covers sourcing, edge cases (missing registry UUIDs, wire-only tools), the canonical-UUID design intent, and lack of side effects. Complete for a zero-parameter introspection tool.

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 has 0 parameters with 100% coverage, so baseline is 4 per rubric. Nothing needed here; the description correctly focuses on behavior rather than inventing parameter semantics. No issue.

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?

Clear specific verb+resource: 'Return canonical (tool_id, mcp_name, …) for every tool the wheel exposes.' Unambiguous scope. It's a list/metadata introspection tool clearly distinct from siblings like brain_list_brains, brain_list_attachments, etc. which all return domain resources — this one describes registry identity mapping, functionally unique.

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 names the consumer ('any client (Studio, agents, FE) that needs to know how this MCP identifies its tools') and gives concrete use context (Reconcile UUID-join against pricing model, no name-based derivation). Even mentions issue numbers (#174, #175). Strong behavioral guidance about when to reach for this tool.

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