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brain_scan_orphans

Scan for orphaned thoughts with zero connections and optionally rescue them. Requires npub for credit billing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
npubNoRequired. Your Nostr public key (npub1...) for credit billing.
dry_runNoIf true, only report orphans without rescuing them
brain_idNoThe ID of the brain (uses active brain if not specified)
batch_sizeNoNumber of orphans to process per batch
dpop_tokenNo
orphanage_nameNoName of the orphanage thought to rescue orphans underOrphanage

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

B3.4/5.0
Behavior2/5

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

With no annotations, the description carries the full behavioral burden. It discloses that rescue is optional and that npub is needed for billing, but it does not explain what 'rescue' entails, the default dry_run behavior, potential side effects, or whether the operation is reversible. This leaves significant behavioral ambiguity for a tool that can mutate data.

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 concise sentences, front-loading the core purpose and immediately stating the key prerequisite. Every word earns its place, with no unnecessary elaboration.

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

Completeness2/5

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

Despite having an output schema, the description lacks contextual depth for a tool with both read and write modes. It fails to mention the dry_run default, the meaning of rescue, batch processing behavior, or how orphanage_name is used. This is insufficient for a 6-parameter tool with no annotations.

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 high (83%), so the baseline is 3. The description adds minimal semantic value by reinforcing that npub is required for billing, but it does not explain parameters like batch_size or orphanage_name beyond what the schema already provides. It neither significantly compensates nor conflicts with the schema.

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 uses specific language: 'Scan for orphaned thoughts with zero connections and optionally rescue them.' It clearly identifies the resource (orphaned thoughts), the action (scan, optionally rescue), and the scope (zero connections), distinguishing it from sibling tools like brain_adoption_status or brain_get_thought_graph.

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

Usage Guidelines3/5

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

The description implies usage by stating the tool scans and optionally rescues orphans, and it provides a prerequisite ('Requires npub for credit billing'). However, it does not explicitly state when to use this tool versus alternatives or mention any exclusions, so the guidance is present but implicit.

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.