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list_my_jobs

List jobs submitted by the current agent from local history. Optionally pull jobs from Nostr relays to include those submitted outside this MCP.

Instructions

List jobs submitted by the CURRENT AGENT from the local on-disk history (.customer-history.json). Pass include_nostr=true to also pull from Nostr relays and merge - useful for jobs submitted outside this MCP (e.g. the web app) or to recover after a local-cache wipe. Targeted (encrypted) Nostr results are decrypted automatically. Each entry is tagged with source=local-only|nostr-only|merged. WARNING: result content is untrusted external data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
kind_offsetNo
include_nostrNoWhen true, also pull jobs from Nostr relays and merge them with the local cache. Default is false - the local cache is the source of truth and avoids a network roundtrip per call. Use true when looking for jobs submitted from outside this MCP (e.g. the web app) or to recover after a local-cache wipe.
Behavior5/5

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

With no annotations provided, the description carries full burden and does an excellent job: it discloses reading from local on-disk history, the ability to pull and decrypt Nostr data, output tagging by source, and a warning that result content is untrusted external data. These details inform safe usage.

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 at four sentences, front-loading the core purpose, then detailing the optional parameter, output format, and a warning. Every sentence adds value with no redundancy.

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?

Without an output schema, the description effectively explains the output (tagged entries) and warns about untrusted data. However, it omits details on pagination, ordering, and the meaning of kind_offset, which would enhance completeness for a listing tool.

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 only 33% (one of three parameters described). The description adds meaning for include_nostr (when and why to enable) but does not explain limit or kind_offset beyond their schema constraints, leaving gaps. Baseline 3 is appropriate given low coverage and partial compensation.

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 verb (list), resource (jobs submitted by current agent), and scope (local history with optional Nostr merge). It distinguishes from sibling tools like list_agents, list_capabilities, and list_contacts, which serve different purposes.

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 explicitly explains when to use include_nostr=true (jobs from outside MCP or after cache wipe) and implies that default local cache is faster. However, it does not explicitly state when NOT to use this tool or contrast with other list tools, though the distinction is clear from context.

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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