Moxlade — Upwork buyer intelligence

Score a posting

get_job_score

Read-onlyIdempotent

How good a posting is on its own terms, 0-100, and WHY.

Use it to rank a shortlist — search_jobs, then this on the ids worth reading in full. upwork_id is the posting's id.

The number is stored on every posting in the corpus; the explanation is recomputed on read, so you always get both — the score and the attributes that produced it, separated into what earned points and what lost them.

It scores the POSTING, not you and not the client's payment history: budget shape, the client's hire rate and feedback score, and the rest named in the breakdown. For whether they actually pay, ask get_buyer_quality. A score with no breakdown would be a claim you cannot check, which is why one never comes without the other.

Feed-scoped: answers for postings this corpus has shown you — your own search results and saved-search matches. Any other id is refused, with how to bring it into reach.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`upwork_id`	Yes	a posting id as search_jobs returns it. Not a URL, and without the leading '~'.

Output Schema

TableJSON Schema

Name	Required	Description
`note`	No	Present when the two scores differ, explaining why.
`basis`	No	What the score is computed from — the posting, not you and not the client's payment history.
`found`	No	False when the corpus holds no such posting.
`score`	No	0-100, recomputed now from the posting as it currently stands. THIS is the one to act on. Present whenever `found` is true; when it is false the whole score is absent and `note` says why.
`breakdown`	No	What earned points and what lost them — see ScoreBreakdown. A score with no breakdown is a claim you cannot check.
`upwork_id`	No	The posting.
`authoritative`	No	Names which field is the score, so two numbers cannot be ambiguous.
`score_at_ingest`	No	Only present when it DIFFERS from `score` — the value written when the posting was first collected, before re-scraping moved its client counters.

Schema Changelog

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

First observedSep 6, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already indicate read-only, idempotent, closed-world behavior, and the description adds meaningful context beyond those: the score is stored, the explanation is recomputed on read, the breakdown separates earned versus lost points, and out-of-feed ids are refused. This gives the agent a clear picture of what to expect without contradicting any annotation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and then proceeds logically through usage, behavior, differentiation, and scope. It is somewhat wordy, but every sentence adds useful information; the structure makes the content easy to scan.

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?

With one well-documented parameter, rich annotations, and an output schema, the description covers everything an agent needs: what the score means, how to use it in the search workflow, what the breakdown contains, when to prefer a sibling tool, and the feed-scoped limitation. Nothing material is missing.

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?

The schema already fully documents upwork_id, including the format, constraints, and what it is not (URL, leading '~'). The description reinforces that it is the posting id and connects it to the search_jobs workflow, but it does not add substantial new meaning beyond the schema. Baseline 3 is appropriate given 100% schema coverage.

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 definition: 'How good a posting is on its own terms, 0-100, and WHY.' This clearly identifies the tool's verb (score), resource (posting), and output (a numeric score plus explanation). It also distinguishes itself from siblings by explicitly separating scoring the posting from evaluating the client or their payment history.

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 gives an explicit workflow: 'search_jobs, then this on the ids worth reading in full.' It also names an alternative, get_buyer_quality, and states exactly when to use it instead: 'For whether they actually pay, ask get_buyer_quality.' The feed-scoped restriction and refusal behavior further clarify when a call will fail.

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.

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