Provides real-time access to verified-escrow GitHub coding bounties, allowing AI agents to list, find matching, get bounty details, and query statistics from GitHub issues.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@bounty-mcp list open bounties for Python"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Live coding-bounty deal-flow as a stdio MCP server. For AI agents finding paid coding work.
Built by weiseer. Probe P-002.
Gives AI agents real-time access to verified-escrow GitHub coding bounties — currently sourced from Algora's algora-pbc[bot] escrow signals, filtered for scam-farm noise (SecureBananaLabs / ClankerNation / UnsafeLabs excluded at scan level).
Your agent can:
list_bounties — query the active pool by language, $ floor, bug-fix-only
find_matching — given a skill set + $ floor, return ranked candidates
get_bounty — full record for one bounty
get_stats — aggregate stats (total, by language, unclaimed, $)
Related MCP server: Archimedes Market MCP
npm install -g @weiseer/bounty-mcp{
"mcpServers": {
"bounty-mcp": {
"command": "npx",
"args": ["-y", "@weiseer/bounty-mcp"]
}
}
}Agent DIY | bounty-mcp | |
Token cost per call | $0.02–0.06 (scrape + parse + filter) | $0 free / $0.00005 paid |
Latency | 2–5 seconds | <100ms |
Scam filter | Agent must dedupe known scam-farms | Built-in (SecureBananaLabs / ClankerNation / UnsafeLabs excluded at source) |
Cross-PR / cross-claim check | Agent must run timeline queries | Pre-computed |
Rate limit risk | 10,000 agents scraping → all blocked | Single coordinated upstream client |
Opire / Polar.sh / IssueHunt — coming as upstream signal sources mature
Cannabis / regulated industry bounties — out of scope
Per-skill matcher beyond language tags — coming in v0.2
See bounties.json in the package. Each bounty record has key, repo, issue_number, title, html_url, dollars, language, attempts, has_open_pr, is_bug_fix, is_assigned, trust, funder_org, issue_created_at, issue_updated_at, seen_at.
BOUNTY_MCP_URL — override the remote bounty snapshot URL (default: https://oracle.weiseer.com/bounties.json)
BOUNTY_MCP_LOCAL_ONLY=1 — skip remote fetch, use bundled snapshot only
@weiseer/llm-oracle-mcp — LLM provider pricing + availability oracle (P-001)
github.com/weiseer — all weiseer services + status
Apache-2.0. Catalog format: MIT.
Probe-pulled. What gets added is what users request via issues.
Find bounties matching a skill set + $ floor. Sorted by $ desc. Excludes assigned/active-PR bounties.
| Name | Required | Description | Default |
|---|---|---|---|
| skills | No | languages/skills, e.g. ['python','rust'] | |
| fresh_only | No | ||
| min_dollars | No | ||
| max_attempts | No | skip bounties with >N attempts (scam-pattern guard) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description provides useful behavior: sorting by $ descending and excluding assigned/active-PR bounties. It could mention more (e.g., result limit, read-only nature), but the given info is sufficient for basic understanding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two effective sentences front-load the purpose and then add behavioral details. Every word adds value; no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the core purpose and key behaviors, but lacks details on output format, result limits, and the meaning of parameters like fresh_only. Given no output schema and 4 parameters, it is slightly incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 50%, and the description adds no extra meaning beyond the schema. It indirectly mentions skills and min_dollars but does not explain them further, nor does it address fresh_only or max_attempts.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (find), resource (bounties), criteria (skill set and dollar floor), sorting (desc by $), and exclusions. It distinguishes itself from siblings like list_bounties and get_bounty as a filtered search tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The purpose is clear enough to infer when to use it: when filtering bounties by skills and minimum dollars. However, it does not explicitly state when not to use it or mention alternatives, leaving some guidance to context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Full record for a single bounty by key (format: 'owner/repo#issue_num').
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It reveals the key format but does not mention behavioral aspects such as whether the tool is read-only, includes any side effects, rate limits, or what 'full record' entails beyond the key. Minimal transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, concise sentence that front-loads the essential information (purpose and key format). No redundant words or phrases.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description adequately covers the key format and scope (single bounty record), but given no output schema, it would benefit from mentioning the structure of the returned 'full record' or any additional context. Acceptable for a simple retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'key' has no description in the schema (0% coverage). The description adds the crucial format 'owner/repo#issue_num', which provides concrete guidance beyond the schema's bare type string.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it returns a full record for a single bounty by key, with the key format 'owner/repo#issue_num'. This clearly distinguishes it from sibling tools like list_bounties (list multiple) and get_stats (statistics).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates when to use this tool (to get a single bounty record), but does not explicitly state when not to use it or compare to alternatives like list_bounties for multiple records or find_matching for searching. The sibling names provide implicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Aggregate stats on the active bounty pool (total, by language, by bug-fix-vs-feature, total $).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description must convey all behavioral traits. It only describes output (aggregate stats) without mentioning read-only nature, side effects, or auth requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no waste, front-loading purpose and details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, no-output-schema tool, description covers key output aspects. Could mention data freshness or caching, but overall complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist (0), so baseline is 4. Description adds meaningful context about output structure (by language, bug-fix-vs-feature), which suffices.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool aggregates stats on the active bounty pool, specifying breakdowns by language and bug-fix-vs-feature, and total $. This distinguishes it from list_bounties (individual listing) and get_bounty (single bounty).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied through the description of aggregated stats, but no explicit guidance on when to use vs. alternatives like list_bounties or find_matching is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List live verified-escrow GitHub coding bounties. Filter by language, $ floor, bug-fix only, exclude already-assigned/open-PR.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| language | No | e.g. python, typescript, rust, go | |
| min_dollars | No | minimum bounty $ amount | |
| bug_fix_only | No | ||
| exclude_open_pr | No | ||
| exclude_assigned | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose all behavioral traits. It only says 'List', implying read-only, but omits details about pagination, sorting, rate limits, auth requirements, or response format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences: the first defines the tool's core functionality, the second enumerates filters. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a list-with-parameters tool, the description covers purpose and main filters. However, it lacks details on output format, pagination, sorting, or error handling, which would improve completeness given no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has only 33% description coverage (language and min_dollars). The description compensates by naming the key filters: language, $ floor, bug-fix only, exclude assigned/PR. It adds meaning beyond the bare schema for most parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List', the resource 'live verified-escrow GitHub coding bounties', and the scope (filterable). It distinguishes from siblings like 'find_matching', 'get_bounty', and 'get_stats'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lists the available filters but does not explicitly state when to use this tool versus siblings. It implies browsing all bounties, but no guidance on exclusion or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.1find_matchingget_bountyget_statslist_bountiesEach tool has a distinct purpose: listing with filters, personalized matching, single bounty detail, and aggregate stats. No overlap in functionality.
All tool names follow a consistent verb_noun pattern using snake_case, making them predictable and easy to parse.
Four tools is appropriate for a read-only bounty index; each tool serves a necessary role without redundancy.
Covers all read-oriented workflows (list, search, detail, stats) but lacks write operations like creating or updating bounties, which may be intentional.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Discover verified deep-tech engineering bounties from any AI agent.
A public bounty board where AI agents do paid work. USDC on Base, paid on accepted delivery.
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