Moxlade — Upwork buyer intelligence

Your saved searches

list_saved_searches

Read-onlyIdempotent

Every search saved against this token, with whether it is currently matching.

Call it to find the slug delete_saved_search needs, to check that a search you saved is actually running, or to see what you had before deciding whether to pay again.

Takes no arguments — it lists what this token owns and cannot see anyone else's. Returns searches: one entry per saved search with its saved slug (the id every other tool takes), the name you gave it, matching — false when the plan has lapsed and the search is paused rather than deleted — the prefilter it runs, and created_at.

Read-only, and readable on every plan state including expired. That is deliberate: someone deciding whether to pay has to be able to see what they had.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description	Default
`searches`	No	Everything saved on this account. It cannot see anyone else's.

Schema Changelog

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

First observedSep 6, 2026

TDQS

A5/5.0

Behavior5/5

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

Beyond the readOnlyHint and idempotentHint annotations, the description adds meaningful behavioral detail: it is intentionally readable on every plan state including expired, matching becomes false when the plan lapses, and paused searches are not deleted. This gives the agent important context about plan-sensitive behavior that annotations alone do not convey.

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 front-loaded with the core purpose, then provides usage guidance, parameter clarification, return field semantics, and access behavior. Every sentence adds value and the length is justified by the useful operational details.

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?

For a parameterless read-only tool with an output schema and rich annotations, the description is fully complete. It covers invocation purpose, scope limitations, return fields, behavior on expired plans, and read-only guarantees, leaving no obvious gap for an agent deciding whether and how to call it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so the schema itself carries no parameter meaning. The description explicitly states 'Takes no arguments' and explains why the token is the implicit scope, which fully compensates for the absence of parameter documentation.

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 precise statement of what the tool does: lists every search saved against the current token, including whether it is currently matching. It clearly distinguishes this from sibling tools like save_search and delete_saved_search by describing its listing scope and purpose.

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?

The description explicitly tells the agent when to call it: to find the slug delete_saved_search needs, to verify a saved search is running, or to see what searches existed before deciding whether to pay again. It also clarifies the tool cannot see other tokens' searches, leaving no ambiguity about its scope.

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

A4.6/5.0

Disambiguation5/5

Each tool maps to a distinct resource or action: search, filter validation, saved-search management, buyer lookup, buyer quality, job scoring, market rates, and account/plan introspection. The get_* tools are cleanly separated by complements such as get_job vs get_job_score and get_buyer vs get_buyer_quality, so an agent should not struggle to pick the right one.

Naming Consistency5/5

All tool names consistently use snake_case verb_noun or verb_adjective_noun patterns, such as search_jobs, check_prefilter, delete_saved_search, and get_buyer_quality. There are no camelCase names, vague verb-only names, or mixed conventions.

Tool Count5/5

At 15 tools, the server sits exactly within the ideal range and each tool earns its place in the workflow. The set covers discovery, enrichment, saved-search lifecycle, and account introspection without obvious redundancy.

Completeness4/5

The core workflow is well covered: search, prefilter validation, job detail, scoring, buyer identity, payment quality, saved-search create/list/delete, and plan/activity visibility. The main gap is the lack of an update operation for an existing saved search, such as changing its filter or attaching a webhook later; this is a minor workaround rather than a blocking dead end.

Resources

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