get_pricingMedian's published pricing: the flat monthly fee by revenue band under $1M in annual revenue, the per-account and per-ledger-entry meter that scoping is based on above $1M, what is included, and add-on pricing for tax, R&D credits, and sales tax.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this tool as read-only and idempotent, so the safety profile is clear. The description adds that the returned data is published pricing and describes its category breakdown, but it does not disclose any further behavioral detail such as whether the data is periodically stale, sourced externally, or the canonical source of truth. No contradiction exists.
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 a single coherent sentence that leads with the resource name and then lists the pricing components in an organized manner. It has no filler, though it is slightly dense with multiple clauses packed in one sentence.
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, read-only information tool with no output schema, the description covers the main pricing dimensions an agent needs. It does not include a detailed return shape, but none is expected here, and the content list is enough to judge relevance.
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 tool takes zero parameters, so there is no input schema meaning to supplement. The 0-param baseline of 4 applies, and the description appropriately focuses entirely on what the output represents rather than parameter handling.
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 names the resource — Median's published pricing — and enumerates the exact content categories (flat monthly fee by revenue band, metered pricing above $1M, included features, add-ons). It is more specific than the title alone, though it does not explicitly distinguish itself from siblings or state a use verb beyond the tool name.
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 provides no guidance about when to call this tool, what question it answers, or when to prefer sibling tools such as get_company_overview or list_services. It names no exclusions and does not help an agent route between alternatives. All usage context has to be inferred from the pricing content.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Tools mostly demand exactly one job: the list/explain/score path is clearly labeled per domain, and the START-HERE markers guide entry points. The fuzzy spots are get_conf_lefts and get_evidence_recipe (both refinement steps after list_compliance_obligations) plus list_services vs get_company_overview (they overlap on what Median does); these are distinguishable from the descritions but are the kinds of pairs that could send an agent the wrong way.
All twelve names follow the same verb_noun snake_case convention with consistent verbs: list the collections (_lists), get facts/computed refinements, explain single-item detail, and score the assessment. The pair structure (list vs explain vs proof) makes the intent predictable from the name for the first decode.
Twelve tools is right within the ideal for a domain-focused server: seven true domain tools (compliance + books) and five content/take-usational helpers. The content cluster (services, overview, pricing, blog, blog_post) is a bit tangential to a compliance/books mission, but this is a company-fronting server, so the zone works with a reason.
Both workflows close their loop: compliance has list → explain → confounders → evidence, and books has list → explain → score, with no obvious dead-end in either. Minor residuals exist (no explicit tool for showing covered versus non-covered states, other collaboration across the two domains), but an agent can work out these as of today's.