Skip to main content
Glama

Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Restore a Deleted Item

restore_deleted
Idempotent

Undo a soft-delete: restores a thesis, watchlist, signal, claim or report that delete_* archived. The record returns to the state it held before the delete — a closed thesis comes back closed, a paused signal comes back paused. When the item was deleted before the server began recording its prior state, prior_status_known is false and the response says which default was used. A restored report returns to its prior status AND visibility, so a report that was public comes back public and one that was private stays private; when that state predates the change that began recording it, the report returns private and prior_status_known is false rather than guessing at publication. Citation overrides are NOT restorable (that delete removes the row outright) — use the approval flow. Idempotent: restoring a live item succeeds and changes nothing. Tier: paid + free (sample rejected).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesThe record's id. For a watchlist this is the `watchlist_id` returned by `delete_watchlist` — NOT its name, because deleting a watchlist frees its name for reuse.
kindYesWhich record type to restore. Citation overrides are not restorable.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
kindYes
_metaYesProvenance envelope — data lineage for every MCP response
statusYes
prior_status_knownYes

TDQS

A5/5.0
Behavior5/5

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

The description adds substantial behavioral detail beyond the annotations: restoring to the exact prior state, handling of unknown prior status with `prior_status_known` and default behavior, report visibility nuance, idempotency, and tier restrictions. No contradiction with annotations; in fact it reinforces `idempotentHint`.

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

Conciseness5/5

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

The description is detailed but every sentence adds unique value: purpose, state restoration, edge cases, idempotency, tier. It is well-structured and front-loaded, with zero filler.

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 and the presence of output schema and annotations, the description covers all critical dimensions: what gets restored, state/visibility nuances, unknown-prior-state behavior, non-restorable types, idempotency, and access tiers. It is fully self-sufficient for an agent to decide and invoke correctly.

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%, so baseline is 3, but the description adds meaningful param semantics: it explains that `id` for watchlists must be the `watchlist_id`, not the name, because deletion frees the name for reuse. This is critical usage knowledge not present in the schema. The `kind` enum is also clarified with the non-restorable exception.

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 opens with a clear verb+resource: 'Undo a soft-delete: restores a thesis, watchlist, signal, claim or report that `delete_*` archived.' It precisely names the affected resource types and explicitly ties to the delete_* siblings, making the tool's purpose unmistakable and distinct from related tools.

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?

It clearly states when to use: to undo a soft-delete. It also gives an explicit exclusion: 'Citation overrides are NOT restorable (that delete removes the row outright) — use the approval flow.' This names the alternative and the condition, satisfying the when/when-not/alternatives bar.

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

All Valuein — SEC EDGAR Fundamentals & Smart-Money Data 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
Disambiguation5/5

Each tool has a distinct purpose with detailed descriptions that clarify differences. Overlaps like get_peer_comparables vs screen_universe are well-differentiated by scope (single company vs cross-sectional). Similarly, get_insider_sentiment vs get_smart_money_flow are clearly distinguished by data sources and methodology.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., create_report, get_financial_ratios, delete_alert). No mixing of conventions or inconsistent verbs.

Tool Count2/5

With 69 tools, the count far exceeds the 25+ threshold for 'too many'. While the domain is broad, the sheer volume likely overwhelms agents and increases selection complexity.

Completeness4/5

The tool set covers a wide range of SEC filings, ratios, smart-money data, alerts, reports, and more. Minor gaps exist (e.g., no options or detailed debt data), but most analyst workflows are supported.

Resources