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personalbrain-mcp

by io.github.lonniev

brain_check_price

Preview the effective cost of a tool call.

Shows the base cost and any constraint effects (discounts, free trials, surge pricing). Free — no credits required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
npubNo
tool_idYesEither the tool's UUID (from the pricing model) or a bare capability string (e.g. ``"deal_scenario"``). FE callers usually have the capability name; this resolves both so the FE doesn't need to derive UUIDs locally.
dpop_tokenNo
tool_kwargsNoOptional JSON object with tool call parameters for ad valorem / categorical-multiplier pricing preview (e.g. '{"amount_sats": 5000}' or '{"difficulty": "sovereign", "mode": "live"}').

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description must carry behavioral disclosure. It reveals key traits: the call is a 'preview' (non-executing), shows base cost and constraint effects, and is free (no credits required). It does not fully describe limitations or authentication needs, but the essentials are covered.

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 two sentences: the first front-loads the purpose, the second elaborates on output and cost. Every part is informative, with no fluff or repetition of schema details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description provides enough context for an agent to understand the tool's purpose, output, and cost implications. Since an output schema exists, return values are covered. However, it does not explicitly distinguish from brain_get_pricing_model, which could raise confusion in the sibling tool list.

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 50% (tool_id and tool_kwargs have descriptions). The description adds context about 'constraint effects' and 'tool call parameters,' which hints at tool_kwargs usage. However, it does not explain npub or dpop_token, which have no schema descriptions, leaving a gap for optional but potentially relevant parameters.

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's function: 'Preview the effective cost of a tool call.' It specifies the verb (preview), resource (cost of a tool call), and what it outputs (base cost and constraint effects). This distinguishes it from siblings like brain_get_pricing_model or brain_check_balance.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description conveys clear usage context: use this to preview cost before executing a tool call. It also notes 'Free — no credits required,' implying a safe, low-cost way to estimate pricing. However, it does not explicitly mention alternatives or when not to use it, which stops it from being a 5.

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.

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