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freelancehunt-mcp

Find projects worth bidding on

find_projects
Read-only

Filters Freelancehunt project feeds by keywords, budget range, bid count, and employer quality to surface only projects worth bidding.

Instructions

Walks several feed pages and filters them locally on criteria the API cannot express: keywords, budget floor, bid-count ceiling and employer quality. Use this to surface the few projects worth a bid instead of reading the whole feed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum projects to return.
formatNosummary
currencyNoOnly projects budgeted in this currency, e.g. UAH.
keywordsNoCase-insensitive; a project matches if any keyword occurs in its title or description.
max_bidsNoSkip crowded projects that already have more bids than this.
max_pagesNoFeed pages to walk (10 projects each). Each page is one API call.
skill_idsNoServer-side skill filter applied before the local pass. Strongly recommended.
max_budgetNoMaximum budget amount.
min_budgetNoMinimum budget amount. Projects with no budget are kept unless drop_negotiable is set.
drop_negotiableNoDrop projects with no stated budget. Off by default — negotiable projects are often fine.
exclude_keywordsNoDrop projects whose title or description contains any of these.
require_employerNoDrop projects whose employer block is hidden (Plus-only listings).
employer_min_ratingNoMinimum employer rating.
employer_min_completedNoMinimum finished projects on the employer's record.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

The description adds useful behavioral context by noting that the tool walks several feed pages and filters locally, implying multiple API calls. The readOnlyHint annotation already covers safety, and the description does not contradict it. It could mention open-world data implications or potential rate limits, but the annotation reduces the need.

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 with no redundant wording. It front-loads the main behavior and use case, making it easy for an agent to quickly understand the tool's purpose.

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?

Given there is no output schema, the description adequately conveys that the result is a filtered set of projects. It does not specify output ordering or exact result shape, but the sibling-tool context and parameter descriptions make the expected result reasonably clear.

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 high at 93%, with most parameters having clear descriptions. The tool description adds framing by tying concepts like 'budget floor' and 'employer quality' to parameters, but it does not materially extend the per-parameter meaning already present in 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 clearly states the tool finds projects by walking feed pages and applying filters that the API cannot express, distinguishing it from plain listing tools. It also names the specific filtering dimensions: keywords, budget, bid count, and employer quality.

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 recommends using this tool 'to surface the few projects worth a bid instead of reading the whole feed,' giving clear guidance on when it is appropriate. It does not explicitly contrast it with sibling tools like list_projects or get_feed, but the intended use case is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.