Skip to main content
Glama

mcp-server

by io.github.forcedreamai

Invoke a ForceDream agent

forcedream_invoke_agent

Invoke a ForceDream agent to do real work. SPENDS your balance — requires authentication (OAuth). Returns the output, what you were charged, and a proof_id you can verify with forcedream_verify_proof. Honest declines and insufficient output are charged 0; never double-charges. Use this for any agent WITHOUT a dedicated tool. For security-scan-v1, data-extract-v1, or lead-score-v1 specifically, prefer forcedream_security_scan, forcedream_extract_data, or forcedream_score_lead instead -- same underlying agents, simpler input shape.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesThe task for the agent to perform.
agent_slugYesThe agent to invoke, e.g. "data-extract-v1". Use forcedream_search_agents to discover.
budget_penceNoOptional max spend in pence for this invocation.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentNo
errorNoPresent on failure, e.g. 'insufficient_balance'.
outputNo
statusYes'completed' or 'error'.
verifyNoURL to verify this proof.
task_idNo
proof_idNo
top_up_urlNoPresent only on insufficient_balance, on the remote OAuth server -- a real, live Stripe checkout URL.
balance_penceNo
charged_penceNo

TDQS

A4.9/5.0
Behavior5/5

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

The description discloses key behaviors: spends balance, requires authentication, returns output/charge/proof_id, honest declines charged 0, never double-charges. Annotations indicate mutability (readOnlyHint=false) but not destructiveness, and the description adds clarity on financial impact and guarantees, going beyond annotations.

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 well-structured with a clear first sentence, followed by crucial behavioral notes, and then usage guidelines. It is slightly verbose but every sentence adds value. The front-loading of purpose and key behaviors is effective.

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?

Given the tool's complexity (general agent invoker with billing and verification), the description covers all essential aspects: purpose, usage, authentication, billing behavior, return values (output, charge, proof_id), and verification path via forcedream_verify_proof. An output schema exists (not shown) but the description explains what to expect.

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?

Schema coverage is 100% with descriptions for all three parameters. The description adds value by explaining that agent_slug can be discovered via forcedream_search_agents and that budget_pence is an optional max spend, providing usage context beyond 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 'Invoke a ForceDream agent to do real work' and distinguishes from sibling tools by naming specific dedicated agents and suggesting alternatives. The verb 'invoke' plus 'agent' with purpose 'to do real work' gives a specific, actionable 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?

Explicitly states when to use this tool ('Use this for any agent WITHOUT a dedicated tool') and when not to, listing three sibling tools as preferable alternatives. Also mentions authentication and cost spending context.

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

All mcp-server toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4/5.0
Disambiguation3/5

Most tools have clearly distinct purposes (fraud vs extract vs generate vs sentiment vs lead scoring vs quote vs proof verification). However, there is notable overlap among the search_* discovery tools: forcedream_search_agents, forcedream_search_reliability, and forcedream_search_costs all surface overlapping agent metadata (success_rate appears in both search_agents and search_reliability), which could cause misselection. Additionally, forcedream_extract_data vs forcedream_extract_entities vs forcedream_extract_action_items overlap somewhat in the extraction domain despite distinct outputs (JSON fields vs raw entities vs action items).

Naming Consistency4/5

The forcedream_ prefix is used consistently throughout, and most tools follow a forcedream_<verb>_<object> pattern (extract_data, generate_code, score_lead, security_scan). However, there is inconsistency in verb style: check vs extract vs generate vs invoke vs search vs verify vs summarize are all different verb types, and the objects don't follow a uniform noun convention (some are actions like invole_agent, others resources like market_quote). The naming is readable and discoverable but not perfectly uniform.

Tool Count4/5

At 17 tools, this is slightly above the ideal range but justifiable given the broad multi-service scope (fraud, extraction, generation, discovery, verification). Each tool maps to a reasonably distinct service capability, and none feel like padding. The count borders on heavy but earns its place given the diverse domain coverage.

Completeness4/5

The tool surface is comprehensive for a multi-purpose AI/ML service platform, covering fraud detection, data extraction, code generation, sentiment analysis, embeddings, lead scoring, security scanning, summarization, market quotes, agent discovery, and proof verification. Missing are update/delete operations, but this appears to be a stateless service rather than a CRUD resource store. The discovery tools (search_* variants) and meta capabilities (verify_proof) round out the lifecycle well, though there's no clear cleanup or batch-processing tool.

Resources