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Glama

personalbrain-mcp

by io.github.lonniev

brain_request_npub_proof

Request npub ownership proof from a patron via Nostr DM.

This is the npub-OWNERSHIP-PROOF flow — use it when a call returns proof_required. It proves the caller controls an npub; it does NOT deliver any service secret. To hand an operator its API keys or OAuth secrets, use request_credential_channel instead.

Sends a challenge DM that the patron must sign and reply to using their Nostr client. This is a human-in-the-loop flow.

After calling this tool, STOP and tell the user to check their Nostr client and reply to the challenge. Wait for the user to confirm they have replied before calling receive_npub_proof. Do NOT poll or retry — each receive_npub_proof call destructively drains the relay mailbox.

Returns a dpop_token — the demonstrated-proof-of-possession token that the calling application MUST remember and pass as the dpop_token parameter on every subsequent paid tool call. The MCP does not retain this value across restarts.

Lifecycle: The cached proof expires after the patron's chosen duration. When it expires, call request_npub_proof again for a fresh challenge, then wait for the user, then call receive_npub_proof.

Free.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonNoOptional. A human-readable purpose for the request ("I'm working on your request XYZ and need the Operator to do ABC for you"). Signed into the provenance attestation and shown in the DM, so the recipient sees *why* they are being asked — especially useful when the signer is unknown to them.
verify_atNoOptional. A free-form statement of WHERE you (the initiating agent) already showed this proof's one-time code to the user — a URL, or "your Claude.ai conversation", "the Grok session". The OAuth 2.0 Device Grant ``verification_uri``, generalized: the user approves only if the code in the DM matches the one you displayed there, so an unsolicited request they've never seen is refused. Signed into the attestation.
patron_npubNoRequired. The patron's npub to request proof from.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / properties / reason
      Added value: +{
+  "default": "",
+  "description": "Optional. A human-readable purpose for the request\n(\"I'm working on your request XYZ and need the Operator to do\nABC for you\"). Signed into the provenance attestation and shown\nin the DM, so the recipient sees *why* they are being asked —\nespecially useful when the signer is unknown to them.",
+  "type": "string"
+}
    • addedInput schema / properties / verify_at
      Added value: +{
+  "default": "",
+  "description": "Optional. A free-form statement of WHERE you (the\ninitiating agent) already showed this proof's one-time code to\nthe user — a URL, or \"your Claude.ai conversation\", \"the Grok\nsession\". The OAuth 2.0 Device Grant ``verification_uri``,\ngeneralized: the user approves only if the code in the DM matches\nthe one you displayed there, so an unsolicited request they've\nnever seen is refused. Signed into the attestation.",
+  "type": "string"
+}
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full burden. It discloses critical behavioral traits: the human-in-the-loop nature, that receive_npub_proof 'destructively drains the relay mailbox', that the dpop_token is not retained across restarts, and that the proof expires. This goes well beyond a minimal description.

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?

Although longer than typical descriptions, every sentence earns its place: it covers what, when, flow, return value, and lifecycle. It is well-structured with line breaks and front-loaded with the core action. The stray 'Free.' at the end is minor and does not detract from clarity.

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?

The tool is part of a complex, stateful human-in-the-loop flow. The description covers the trigger condition, the exact sequence of steps, the return token's handling, and expiration behavior. It even notes the alternative tool. An agent has everything needed to execute this tool safely and effectively.

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 coverage is 100%, so the baseline is 3. The description does not add parameter-specific details beyond what the schema already provides, but it does contextualize the overall flow. The schema descriptions for reason, verify_at, and patron_npub are already thorough, so the description doesn't need to compensate.

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 specific verb+resource: 'Request npub ownership proof from a patron via Nostr DM.' It clearly identifies the tool's function and distinguishes it from the sibling tool by stating that for handing over API keys or OAuth secrets, one should use 'request_credential_channel' instead.

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: 'use it when a call returns proof_required.' It provides a step-by-step flow (call, STOP, tell user, wait, then receive_npub_proof) and warns against polling/retrying. It also names an alternative tool for a different use case, giving clear when-to-use vs. when-not-to guidance.

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