PolyBridge
Server Details
Calibrated probabilistic foresight for AI agents, powered by live prediction-market signal.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.5/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one for generating probability forecasts from prediction markets, the other for searching markets. Descriptions explicitly state when to use each, eliminating ambiguity.
Both tools follow a consistent snake_case pattern with the 'polybridge_' prefix, using action verbs (forecast, search) that accurately reflect their functionality.
With only two tools, the server covers the core actions of searching and forecasting. While minimal, it's well-scoped for the narrow domain of prediction market queries.
The server provides the essential operations for querying prediction markets: finding relevant markets and generating forecasts. Minor gaps like market history or detailed outcomes don't undermine the primary use case.
Available Tools
2 toolspolybridge_forecastPolyBridge ForecastARead-onlyInspect
Generate a read-only probability forecast for a clearly stated future event by searching relevant prediction markets and synthesizing evidence. Use when the user asks for a probability, outlook, or forecast; use polybridge_search when they only need market discovery. Does not place trades, provide financial advice, or access private/internal data.
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | Clearly stated future-event forecasting question, 500 characters or fewer. | |
| include_graph | No | Whether to include the causal graph in the structured response. |
Output Schema
| Name | Required | Description |
|---|---|---|
| error | No | |
| metadata | No | |
| question | Yes | |
| reasoning | Yes | |
| confidence | Yes | |
| latency_ms | Yes | |
| request_id | Yes | |
| engine_type | Yes | |
| probability | No | |
| causal_graph | No | |
| distribution | No | |
| markets_used | Yes | |
| confidence_interval | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds further behavioral context: 'Does not place trades, provide financial advice, or access private/internal data,' which is valuable but not essential given the annotations.
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?
The description is concise with three sentences, front-loaded with the core purpose, and no wasted words. Every sentence adds value.
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?
Given the tool's complexity, presence of an output schema, and comprehensive annotations, the description covers all necessary context: use cases, limitations, and behavioral traits. It is 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?
Schema description coverage is 100%, so the baseline is 3. The description does not add additional meaning beyond what is already in the schema parameter descriptions, such as the question length limit.
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 tool's purpose: generating a read-only probability forecast by searching prediction markets and synthesizing evidence. It uses specific verbs and resources, and distinguishes itself from the sibling tool polybridge_search.
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 explicitly states when to use this tool (when a user asks for probability/outlook/forecast) and when to use the alternative (polybridge_search for market discovery). This provides excellent guidance for the AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
polybridge_searchPolyBridge SearchARead-onlyInspect
Search public prediction markets for markets relevant to a natural-language topic or question. Use when you need candidate markets, market URLs, outcomes, statuses, and relevance scores. Do not use for a final probability forecast, market-history lookup, trading, or private/internal data. Scores are relevance scores, not probabilities.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural-language topic or question to match to public markets. | |
| filters | No | Optional market filters. Currently only filters.status is supported, with active, closed, or resolved. | |
| dimensions | No | Search dimensions to run. Uses all supported dimensions by default. | |
| top_k_per_dimension | No | Maximum number of candidate markets to return per search dimension. |
Output Schema
| Name | Required | Description |
|---|---|---|
| query | Yes | |
| results | Yes | |
| warnings | No | |
| request_id | Yes | |
| total_markets | Yes | |
| dimensions_returned | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds value beyond annotations by clarifying that scores are relevance scores, not probabilities. Annotations already indicate read-only and open-world behavior. No contradictions.
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 sentences plus a crucial final note. Every sentence is informative and necessary. 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?
Description covers what the tool returns (markets, URLs, outcomes, statuses, relevance scores) and when to use it. Given the presence of an output schema and clear annotations, no additional details needed.
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 coverage is 100% with detailed descriptions for each parameter. The description adds minimal extra meaning beyond what schema provides, so baseline score of 3 is appropriate.
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 searches public prediction markets for natural-language topics. It distinguishes from sibling tool polybridge_forecast by explicitly stating what not to use it for.
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?
Explicitly states when to use (candidate markets) and when not to use (forecast, market-history, trading, private data). Provides clear context for appropriate invocation.
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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{
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"maintainers": [{ "email": "your-email@example.com" }]
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