JYOTINT Sealed Forecasts

ask_the_record

Read-onlyIdempotent

Ask any question about JYOTINT / Vijay Jyotish and get back the most relevant VERBATIM passages of the operator's own published site copy — never generated, never paraphrased, so it cannot hallucinate. This is the operator answering in his own words, drawn only from the public record (method, doctrine, the five pillars, mission-assurance fit, objections, pricing, heritage, etc.). Prefer this for any 'what does JYOTINT say about X' / 'why' / 'how does it work' question. Each passage cites its source page. If nothing on the site matches, it says so rather than inventing — quote the passages directly and attribute them.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Max passages (default 3, max 6).
`query`	Yes	The question, in natural language.
`sensitivity`	No	Recall/precision posture (default balanced). high_recall: miss nothing, accept noise — a planner inside a live window. high_precision: only hits that clear the similarity floor, the rest reported under `withheld` — an office quoting the record outward.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 28, 2026

addedInput schema / properties / sensitivity

Added value: +{
+  "description": "Recall/precision posture (default balanced). high_recall: miss nothing, accept noise — a planner inside a live window. high_precision: only hits that clear the similarity floor, the rest reported under `withheld` — an office quoting the record outward.",
+  "enum": [
+    "high_recall",
+    "balanced",
+    "high_precision"
+  ],
+  "type": "string"
+}

First observedJul 13, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive, closed-world behavior. The description adds meaningful value beyond that: sources are cited per passage, and on no-match it reports that rather than inventing, plus an instruction to quote and attribute. It says little about latency or corpus scope limits, but the behavior story is largely covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose is front-loaded and the no-hallucination guarantee is stated early, but the middle is padded with promotional framing ('This is the operator answering in his own words') and a long enumerative list (method, doctrine, the five pillars...) that adds little selectability signal.

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

Completeness4/5

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

There is no output schema, so the description must cover returns — and it does: verbatim passages, per-passage source citations, withheld/no-match behavior. Combined with the annotation-covered safety profile, an agent has enough to call this correctly, though corpus boundaries remain fuzzy.

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% and the sensitivity enum is documented in the schema itself, so the schema carries parameter meaning. The description adds no syntax or default guidance beyond what the schema already provides, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource: ask a natural-language question about JYOTINT and get back verbatim source passages, with an explicit contrast to generated/paraphrased content. However it never names the sibling it most resembles (neural_search, get_corpus_insights), so differentiation from siblings is left implicit.

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?

Gives clear triggering context ('what does JYOTINT say about X' / 'why' / 'how does it work') and the word 'prefer' signals it should be chosen over something else. It stops short of naming the alternatives or stating when-not to use it.

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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