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TensorFeed

by ai.tensorfeed

route_verdict

PREMIUM (1 credit, $0.02). The signed model-routing decision: for a task (code, reasoning, creative, general) or a named model, returns the single best model to use right now, fusing pricing, benchmark capability discounted for contamination, real production usage, measured p95 latency, and live operational state, with runners-up and an AFTA-signed receipt over the inputs. Pay per call with an x402 wallet payment (arguments.payment, or an X-PAYMENT header; strict HTTP-402 transport at https://mcp.tensorfeed.ai/mcp?x402=strict) or use an Authorization: Bearer tf_live_... credits token. No token and no USDC? Claim free trial credits by signing a wallet message at https://tensorfeed.ai/api/payment/trial-credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskNoOne of code, reasoning, creative, general. Provide task or model.
modelNoCanonical model id or display name to narrow the verdict to one model.
paymentNoOptional base64 x402 payment payload (the same string an X-PAYMENT header would carry). Sign against the accepts[0] requirement returned by an unpaid call. Alternative to the Bearer credits token.
exclude_deprecatedNoDefault true. Pass "false" to keep deprecated or sunsetted models.
max_latency_p95_msNoDrop candidates whose measured p95 latency exceeds this floor.
require_operationalNoDefault true. Pass "false" to keep candidates known down or in failover.

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries full burden. It discloses cost, payment methods, and that the result is signed. However, it does not cover error handling, return format details, or rate limits, leaving gaps in behavioral understanding.

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 a single dense paragraph with key info front-loaded (purpose, cost). It could be more structured, but every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description adequately covers purpose, inputs, and cost but lacks details on output format, success/error conditions, and example usage. For a complex paid tool, more completeness would be beneficial.

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 description coverage is 100%, so the description adds only marginal value, such as clarifying the payment parameter's role. The baseline of 3 is appropriate as the schema already explains parameters well.

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 returns 'the single best model to use right now' for a task or named model. It specifies the verb 'returns' and resource, and distinctively positions itself against siblings which are unrelated data retrieval tools.

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 explains when to use the tool (for task or model routing) and the context of paid calls. It does not explicitly state when not to use or mention alternatives, but the sibling tools are clearly different, so no confusion.

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

A3.6/5.0
Disambiguation3/5

Several security/vulnerability tools (check_ai_supply_chain_risk, get_cve_record, get_osv_advisory_by_id, get_osv_advisory_for_package) have overlapping purposes, making it potentially confusing to choose the right one. Other tools are more distinct, but the ambiguity in this cluster lowers the score.

Naming Consistency3/5

All names are snake_case and mostly follow a verb_noun pattern, but the verbs are inconsistent (check, get, list, lookup, query, register, route, search, submit, whats_new). Some tools use 'get' while others use 'check' for similar retrieval actions, and 'whats_new' does not fit the verb_noun pattern.

Tool Count2/5

With 27 tools, the count exceeds the 25 threshold for 'too many', even though the broad scope spans many domains. The sheer number makes the server feel heavy and harder to navigate.

Completeness3/5

The server covers a wide range of data domains, but notable gaps exist: no management of watch subscriptions (only register), no CVE search, and no model search. Write operations are minimal, leaving some workflows incomplete.

Resources